Embed Size (px)
Citation preview
Department of Economics
Master’s Dissertation
In Partial Fulfillment
Of the Requirements for the Degree of
Master of Business Administration (MBA)
Location-based Mobile Marketing
Submitted By: Hajihosseinkhabaz, Saeed
Enrolment No.: 4061874
Date of Birth: 17.02.1987
Degree Course: International Trade (MTR)
1st Supervisor: Prof. Dr. Daniel Michelis
2nd Supervisor: Stefan Stumpp
Submission Date: 26.09.2017
Bernburg, Germany
i
Acknowledgement
I would like to thank my family and friends for their continuous support during the time
of developing my master’s thesis. I’d like to also thank Pro. Dr. Daniel Michelis and
Mr. Stumpp for supervision of my work.
Berlin, 18 September 2017
Saeed Hajihosseinkhabaz
ii
Abstract
The sweeping impact of the tech world on everyday functions of our lives in the past
several years is very well understood. Much of what is acknowledged to be the virtual
platform for people to express themselves has become an inseparable part of the modern
human identity that takes comfort and joy in offering their sense of individualism and
therefore demand an entitlement to being globally approved and emotionally
recognized. The binding essence of the modern age we know called the internet has
taken notions as anonymity and turned them into exposure at great pace. The facilitation
that is brought by the internet is not only a gift of internet itself but also a gift of its
products. Every consumer of any background, any race or nationality is leaving a trace
of digital identity behind as they make transactions in the online and offline world of
business. The usage of this data is nowadays an essential part of marketing the products
that are introduced to consumers on daily basis.
According to the literature review on the subject of location-based mobile marketing,
there is a significant gap in the research regarding the issue. This gap is identified to be
more leaning towards the user-centric approach rather than the technological one.
Based on this information, the author aims to answer the question: “what are the
implications of location-based advertising in mobile marketing?” By answering this
question, the author will provide the necessary framework through which the findings
of the survey can be generalized to the larger population of mobile users while
providing data from secondary sources in order to justify the claims of analysis and thus
make suggestions for marketers in a practical way of thinking as well as
recommendations for future research.
The analysis of the findings includes the four aspects of users’ awareness of location
services, popular connectivity types, popular incentives types and the effect of
incentives on users’ attitude towards location-based mobile marketing.
The findings indicate that mobile users’ awareness about location services is
questionable. Wi-Fi can be dubbed as the most popular type of connectivity. Cash
rewards and store credit are first and second in most interesting types of incentives; and
finally, incentives affect the mobile users’ attitude towards location-based mobile
marketing in a positive way.
Keywords: big data, incentivized ads, location-based advertising, location-based
mobile marketing, smartphone
iii
Table of Contents
1. Introduction ................................................................................................................ 1
1.1. Background ........................................................................................................................... 1
1.2. Problem .................................................................................................................................. 3
1.3. Purpose ................................................................................................................................... 4
1.4. Perspective ............................................................................................................................ 5 2. Frame of Reference .................................................................................................... 6
2.1. Big Data ................................................................................................................................... 6
2.2. Location-based Services ................................................................................................... 7
2.3. Applications of LBS ............................................................................................................ 7
2.4. Components of Location-based Services ................................................................... 9
2.5. Marketing Services in LBS ............................................................................................. 10
2.6. Positioning Techniques ................................................................................................... 10
2.7. Location Technologies..................................................................................................... 11
2.8. Location-based Marketing ............................................................................................. 18
2.9. Case Studies ........................................................................................................................ 19
2.9.1. Dunkin’ Donuts .......................................................................................................... 19
2.9.2. Pizza Hut ...................................................................................................................... 20
2.9.3. North American Auto Brand ................................................................................. 21
2.9.4. Flip Flop Shops .......................................................................................................... 22
2.9.5. Tokyo Smoke .............................................................................................................. 22
2.9.6. Mondelez International & Couche-Tard ........................................................... 23 3. Methodology ............................................................................................................ 25
3.1. The Research Question ................................................................................................... 25
3.2. Literature Review ............................................................................................................. 25
3.3. Delimitations ...................................................................................................................... 25
3.4. Methodological Considerations ................................................................................... 26
3.4.1. Definition of Research ............................................................................................ 26
3.4.2. Research Methodology ........................................................................................... 26
3.4.3. Types of Data .............................................................................................................. 27
3.4.4 Sampling ....................................................................................................................... 28
3.4.5. Credibility of Findings ............................................................................................ 29
3.5. Conceptual Framework .................................................................................................. 29
3.5.1. Testing the Hypothesis ........................................................................................... 31 4. Empirical Findings and Analysis ............................................................................. 33
4.1. Statistical Considerations .............................................................................................. 33
4.2. Descriptive Analysis ......................................................................................................... 34
4.2.1. User’s Awareness ...................................................................................................... 34
4.2.2. Connectivity Types ................................................................................................... 35
4.2.3. Incentive Types ......................................................................................................... 36
4.3. Inferential Analysis .......................................................................................................... 37
4.3.1. Cronbach’s Alpha Test ............................................................................................ 38
4.3.2. Kendall’s Ranked Correlation Coefficient........................................................ 39
4.3.3. Binary Logistic Regression ................................................................................... 39 5. Discussion ................................................................................................................ 42
5.1. Explaining the Findings .................................................................................................. 42
5.2. Truth Criteria ...................................................................................................................... 47
5.3. Limitations........................................................................................................................... 48
iv
6. Conclusion and Recommendations .......................................................................... 49
6.1. Conclusion ........................................................................................................................... 49
6.2. Recommendations ............................................................................................................ 50 7. List of References .................................................................................................... 52 8. Appendices ............................................................................................................... 56
8.1. Questionnaire ..................................................................................................................... 56 9. Declaration of Authenticity ...................................................................................... 59
List of Abbreviations
CRM Customer Relationship Management
CTR Click-through Rate
EGNSSA European Global Navigation Satellite System Agency
GNSS Global Navigation Satellite System
GPS Global Positioning System
LBA Location-based advertising
LBM Location-based Marketing
LBS Location-based Services
MMS Multimedia Messaging Service
NFC Near-field Communication
OS Operating System
ROI Return of Investment
RTLS Real-time Location Systems
SMS Short Message System
v
List of Figures
Figure 1. Shipments of GNSS devices by region (2017) ............................................... 2 Figure 2. Shipments of GNSS devices by type (2017) .................................................. 2
Figure 3. The Basic Components of LBS .................................................................... 10 Figure 4. Geofencing mixed with beacon proximity areas .......................................... 17 Figure 5. A comparison between Geofencing and Beacon Technology ...................... 17
List of Tables
Table 1. Using Location Services Function ................................................................. 34 Table 2. Using Location Apps ...................................................................................... 34
Table 3. Connectivity Types Ranked by Popularity ..................................................... 35 Table 4. Wi-Fi Ranking ................................................................................................ 35 Table 5. Cellular Data Ranking .................................................................................... 35 Table 6. Bluetooth Ranking ......................................................................................... 36 Table 7. Near-field Communications Ranking ............................................................ 36
Table 8. Incentive Types Ranked by Popularity........................................................... 36 Table 9. Store Credit Ranking ...................................................................................... 37 Table 10. Loyalty Points Ranking ................................................................................ 37
Table 11. Cash Rewards Ranking ................................................................................ 37 Table 12. Coupons Ranking ......................................................................................... 37 Table 13. Reliability Statistics ..................................................................................... 38
Table 14. Item-Total Statistics- Initial – Incentives ..................................................... 38
Table 15. Item-Total Statistics- Final – Incentives ...................................................... 38 Table 16. Kendall’s Ranked Correlation Coefficient ................................................... 39 Table 17. Omnibus Tests of Model Coefficient ........................................................... 40
Table 18. Model Summary ........................................................................................... 40 Table 19. Hosmer and Lemeshow Test ........................................................................ 40
Table 20. Classification Table – Block 0 ..................................................................... 41 Table 21. Classification Table – Block 1 ..................................................................... 41 Table 22. Variables in the Equation ............................................................................. 41
1
1. Introduction
In this chapter, we introduce the subject of location-based marketing and advertising and go
over the background of the location-based services as well as discussing the problem and
therefore founding the basis of the purpose of the research.
1.1. Background
According to Firth, time and time again, technology has proven us that the barriers of
limited communication are one by one removed and therefore human beings have been
able to transcend into a state of seamless interaction that allows for limitations of
physical space to be overcome especially in the terms of information. The role of mobile
devices as locative media is not genuinely a new topic as the core idea suggests (Firth,
2015). We have been long using GPS functions in various tools as simple as one our
most convenient ways of travel which is our personal cars (Firth, 2015). Location-based
services have been becoming more attractive as they are seen as the means to
revolutionize mobile communications and target the users with ads that are founded on
personalized context (Dhar & Varshney, 2011).
With the tremendous growth of mobile use worldwide, mobile apps have come to
undertake the functions of location-based information to their benefit (Firth, 2015). One
of the prominent examples of such functionality is Yelp; thus such apps as Yelp and
Google Maps have been able to apply location awareness as the basis of the service
they provide (Firth, 2015).
The commercial indications of location awareness for mobile advertising is one that
pursues additional revenue generated by using the same infrastructures in order to
maximize the ROI by applying strategic marketing practices that keep the cost
minimum to the mere digital application of marketing tactics. To be precise, the value
for marketing using location-based technology does not restrict itself to only advertising
but also the whole process of marketing; from development of the service or product to
the final assessment of the brand by monitoring the user interaction (Verrinder, 2011).
This procedure provides a chance for marketers to zoom in on the trends left behind by
the users so that they can reap the analytical information for continuous improvement
of ads and what they promise (Verrinder, 2011).
According to a report by the European Global Navigation Satellite Systems Agency in
2017, the revenues of context-aware smartphone apps will reach €30.6 billion by 2019
from the €11.7 billion in 2015. The largest regional LBS market since 2011, Asia-
Pacific took up to 50% of the global LBS shipments in 2016 amounting to roughly 1
2
billion devices. North America accounted for 320 million and the EU for 200 million
in the same year (EGNSSA, 2017). The following chart, shows the global shipment of
GNSS (Global Navigation Satellite System) as a whole:
Figure 1. Shipments of GNSS devices by region (2017)
Not only the significance of shipment of LBS-capable devices is worthy of attention,
but also considering the fact that mobile devices outnumber the share of other devices
is essential. The large number of mobile devices, indicates the opportunity for
developers of apps to apply the economies of scale more effectively. Driven by context-
aware smartphone app market, the revenues relative to GNSS rose from €150 million
in 2010 to €5 billion in 2016 (EGNSSA, 2017). The chart in the next page shows the
shipping share of other devices capable of providing location-based services on a global
scale:
Figure 2. Shipments of GNSS devices by type (2017)
Location-based services also bring a lot to table for the user experience. From this
standpoint, the users are welcomed with high-quality engagement that has clear
methods to drive user experience (Rao & Minakakis, 2003).
3
The importance of providing services that apply location-based services lies in the
growing user interaction with location data through mobile devices. A survey done in
the US found that 74 percent of adult mobile owners use their phones to find
information about their surrounding space (Zickuhr, 2013); such implications led for
communication scholar Adriana de Souza e Silva to come up with the notion of “hybrid
space” as an indication of how location-based services as a digital platform merge with
the physical environment that surrounds us (Firth, 2015). According to de Souza, a
hybrid space is formed by three main elements; social interaction, digital information
and physical space (Firth, 2015). These three elements are intertwined in a way, that
the digital information becomes an inseparable part of the physical space because of
user interaction as a binding factor (Firth, 2015).
1.2. Problem
The location-based advertising on mobile devices has been studied with the use of
MMS and SMS services; this is largely due to the fact that these tools were among the
first to be incorporated in LBA as the most traditional and acceptable ways of reaching
customers on location-based terms of marketing (Bauer & Strauss, 2016). On the other
hand, many publications have contributed to researching the web-based advertising
relative to the location of the customers. Research on web-based advertising may be
very different when discussing the reach for mobile users while application of LBA in
mobile as this type of advertising is directly related to the users’ current location (Bauer
& Strauss, 2016). Namely a mobile device is a much more personal device compared
to a computer (Abowd, 2012) and the possibilities of adaptation on mobile devices is
more versatile (Yuan & Tsao, 2003).
A survey carried out by Skyhook found that 80% of app users understand that location
is essential to selected app experiences and 40% of the users tend to turn their location
services setting off chiefly because of privacy concerns (Ward, 2016). A recent study
at Carnegie Mellon University found that the location of some users was shared more
than 5000 times a particular space over the course of two weeks without them ever
knowing about it (Ward, 2016). Tony Costa, a principal analyst at Forrester, claims that
if people are being tracked in a retail store and know nothing about the exchanged value
or what they are compensated for in return, it is beyond an invasion of privacy (Ward,
2016).
4
Other than users’ privacy concerns as an issue, acquiring correct location information
from users’ mobile devices is of great importance. As Ken Parnham, the general
manager for Europe@Near indicates, the main challenge for location-based advertising
is obtaining accurate location data and to match users across multiple data sources. He
adds that on an industry level, standardization on a bigger scale to keep data reliable
and accommodating the increasing number of data providers help overcome the barriers
of LBA (Ward, 2016).
A lack of accuracy standards as well as market education in terms of what certain data
needs to be pulled with consideration of important data elements, misinterpretation of
location data, fresh data or stored data, overwhelming data as a result of dense
population such as multi-story buildings and speed as a factor for tracking the user
device are some of the challenges in obtaining accurate location data according to
Mobile Marketing Association (Ward, 2016).
While accessing location data, given the technological advances that have been made,
might be easy, reaching the approval of consumers could prove difficult. A study by
Boxever, a customer intelligence firm in the travel industry, showed that while 60% of
consumers want offers that are relative to their location and what they’re doing, a
margin of 62% expressed that they do not approve of brands tracking their location
(Surgue, 2015).
1.3. Purpose
In line with the current challenges regarding the application of location-based
advertising in mobile marketing and the limited number of user-oriented studies carried
out by publications, this thesis is tailored to focus on the user attitude towards LBA in
mobile devices. From this viewpoint, we will accommodate users’ demands for the
exchange of the data that they’d be knowingly sharing with marketers in a sense that
advertisement is not meant for bombarding the consumer with ads, but rather structured
to provide incentives to represent a real value for adherence of mobile users to
campaigns designed by marketers.
It is crucial to mention that the purpose of this research is to help with the current lack
of studies that focus on the users’ opinion of location-based mobile marketing. While
measuring attitude, the author aims to find a relationship between the role of incentives
and the acceptance of location-based mobile marketing. In other words, permission or
5
privacy for that matter is regarded as a barrier which can be overcome by using
incentives as a motivator for mobile users to willingly share their location.
1.4. Perspective
This study aims to shape an understanding on what users believe the location-based
mobile marketing should bring to the table as big data continues to grow and dominate
every aspect of marketing and advertising. From this viewpoint, it is more about the
awareness that users have about their location data being used and what they would or
would not ask for in return for the trail of information they leave behind.
As location-based mobile marketing has seen a fair share of research in technological
sense, it is vital to bring the research forward by applying the possibilities of
technological advancements in studies that present these developments to the practical
dimension of driving and generating revenue.
6
2. Frame of Reference
In this chapter, we explain the science behind location-based services and its application in
various fields of digital marketing and case studies in order to justify the methodological
approach chosen for obtaining the survey data in the methodology chapter.
2.1. Big Data
The digital platform provided by internet on which millions of users share their data,
has given birth to the phenomenon of big data (Purcell, 2013). Big data comprises of
three types of data; structured data, semi-structured and unstructured data. Structured
data are the formatted data used in database management systems. Semi-structured and
unstructured data include the non-formatted data that come from multimedia and social
media content (Purcell, 2013).
The surge in information gathering will carry on to go up more than 2.5 quintillion
bytes of data being collected now every day (Statler, 2016). The other moving factor in
big data is the emergence of the Internet of Things (IoT) by embedding tremendous
amount of sensors in every day tools such as cell phones, vehicles and vending
machines to industrial machinery, smart meters, retail stores and so on. The amount of
data stored by tech giants such as Google, Microsoft, Amazon and Facebook is
astonishing (Statler, 2016). Facebook as an example, claimed in 2010, that it had the
world’s largest Hadoop cluster with 21 petabytes of storage. By mid-2012, their storage
of data increased to 100PB as the company was processing 2.5 billion pieces of content
and over 500 terabytes of data every day. Facebook reached an amount of 600PB by
early 2014 (Statler, 2016).
Business intelligence and analytics have become an essential part of today’s
corporations and academic institutions. An IBM Tech Trend Report from 2011, found
that business analytics was among the four major trends in technology (Chen, et al.,
2012). In other survey by Bloomberg BusinessWeek, it was indicated that 97 percent
of corporations whose revenues exceeded and amount of 100 million dollars, used some
form of business analytics in their operations (Chen et al., 2012). The growth of big
data analytics in businesses is so fast and dominating that according to Mckinsey Global
Institute, the US alone will face a shortage of 140,000 to 190,000 people with deep
knowledge of analytical skills as well as a lack of 1.5 million managers who possess
the skills to use and analyze big data in order to enable effective decision making
(Manyika et la, 2011).
7
As many companies are planning to capitalize on this huge flow of data, they are
building data-center infrastructure on a massive scale to be able to store all of it.
Therefore, the analytics that we have come to know as big data is a must have core
competence of any business that wants to stay relevant in today’s commercial world
(Statler, 2016). The data generated from web site click-stream made the need for big
data analysis tools and techniques clear and now with beacon technology is creating
sensor events that correspond to foot-stream data that bring about more opportunities
as well as challenges in this field (Statler, 2016).
Such findings prove that undertaking a proper application of data analysis in business
models can highly affect the end result of corporate well-fare and that’s why this study
is focusing on location metadata as the means for targeted mobile advertising.
2.2. Location-based Services
Some of the definitions for location-based services in the related literature can be found
below:
"Location Based Services are information services accessible with mobile devices
through the mobile network and utilizing the ability to make use of the location of the
mobile device." (Steiniger et al., 2006, p. 2).
“Location-based services (LBS) are the delivery of data and information services where
the content of those services is tailored to the current or some projected location and
context of a mobile user.” (Brimicombe, 2009, p. xiii).
“Location-based Services are IT services for providing information that has been
created, compiled, selected, or filtered taking into consideration the current locations
of the users or those of other persons or mobile objects."(Küpper, 2005, p. xi)
2.3. Applications of LBS
There are a number of fields that location-based services can be implemented given the
proper platform is provided. Some of these functions as navigation has been long
applied in everyday use and some of them as marketing and social media has been
seeing growth in the past years. A futuristic concept in which LBS can provide the most
innovative element is augmented reality. In this section, we go over these categories
(Steiniger et al., 2006).
Emergency Services:
Evidently, the use of location-based services in emergency services and first-aid
assistance has been long known to be one the most effective ways to react as quickly as
8
possible to the persons injured in accidents that for reasons of trauma or distress are
unable to pin-point their location to ask for medical help. This includes both public and
private emergency services for pedestrians and drivers. With regulation of public
services by public organizations, the emergency roadside assistance for drivers appears
to be one of the promising assistance services for operators in terms of revenue
(Steiniger et al., 2006).
Navigation Services:
As one of the most common services made possible through LBS technology,
navigation is largely based on mobile users. The mobile network’s ability to locate a
mobile user can be presented in a number of navigation-based services (Steiniger et al.,
2006).
Information Services:
Getting help with navigation, accessing traffic information, finding the nearest service
spot or obtaining the city map are just a few examples of location-based services. These
location-based services are mostly the digital distribution of location information based
on user location, specification of time and user behavior. A prime example of these
services is tourist guides and travel services that provide the information for nearby
point of interest whether they’re historical monuments or restaurants that are in the
vicinity of the user (Steiniger et al., 2006).
Tracking and Management Services:
A common example of application of location-based services in tracking and
management systems is its use in logistics as for companies to have exact details of
their packages. From a management perspective, tracking vehicles such as ambulances
gives the chance for operators to manage the calls in shortest amount of time possible.
Companies with field personnel can use tracking to dispatch engineers or salespeople
to customers in area to deliver a quick-response service. It also makes for a great
opportunity in supply chain management to apply mobile supply chain management
(Steiniger et al., 2006).
Billing Services:
Location-smart billing service gives the mobile location service provider the ability to
dynamically charge the user of a particular service based on location when accessing
the various types of services (Steiniger et al., 2006).
9
Augmented Reality:
With augmented reality being one of the trends in the current perspective of tech, the
application of location-based advertising incorporated with augmented reality is a
promising field for advertisers and marketers. With computer generated graphics
intertwined into real imagery on our mobile devices, the line between the two will be
blurred even more. The entertainment business is already seeing growing interest in
investing in virtual reality gadgets that plan to alter the world of digital entertainment
tremendously (Steiniger et al., 2006).
2.4. Components of Location-based Services
According to Ashwini and Usha, in order to provide the platform for location-based
services, there are five basic components needed to build the infrastructure for LBS
(Ashwini & Usha, 2014):
Mobile Devices: A tool for the user to request information. Basically any device
with location-enabled hardware such as cellphones, cameras, laptops and so on
that can receive information in from of speech, picture, text and etc (Ashwini &
Usha, 2014).
Communication Network: The mobile network plays an important role in LBS
by transferring the data from the mobile terminal to the service provider and the
requested information from service provider to the mobile device user (Ashwini
& Usha, 2014).
Positioning Component: For the requests that are transferred by the mobile
network to be processed, the service provider needs to find the location of the
mobile devices. Depending on the users’ physical surroundings, a range of
technologies from Global Positioning Systems (GPS) for outdoor positioning,
or active badges, radio beacons for indoor positioning can be used. Manual
specification of location by user is possible when automatic positioning cannot
be determined (Ashwini & Usha, 2014).
Service and Application Provider: The service provider offers a range of
services to users; namely, calculation of position, finding a route, searching
through yellow pages relative to location and searching for information on
objects of interest for the user (Ashwini & Usha, 2014).
Data and Content Provider: The data requested by the user is maintained and
stored by the service provider and by the industry partners and maintaining
10
authorities who provide location-based information (Ashwini & Usha, 2014).
Here are the basic components of LBS:
Figure 3. The Basic Components of LBS
2.5. Marketing Services in LBS
Location-based services that can overtake a marketing role are categorized into two
major types in accordance with the information delivered to the user:
Pulls services deliver the information that are directly requested by the user. An
example could be a user typing in the address of a certain website into the
address field of the browser. Pull services can be further classified as functional
services like calling a taxi or looking for a restaurant in a specific neighborhood
as an example (Ashwini & Usha, 2014).
Push services deliver information that are not requested by the user or are
request in an indirect way which are activated by an event be it a location or
time element. The information that is indirectly requested could be for instance,
a news subscription for a city due to location, advertisement or warning
messages. Push services are more complex to establish as they are not bound by
previous user interaction with the device (Ashwini & Usha, 2014).
2.6. Positioning Techniques
Positioning techniques can be classified into two groups (Ashwini & Usha, 2014).:
1. Network-based Positioning: To track and evaluate user location, the base
network station is used. In this system, the device is sensed by the network; it
could also be the one sending the signal to the network (Ashwini & Usha, 2014).
2. Terminal-based Positioning: The location is determined by the user device
based on the signals received from the base station. Global Positioning Systems
(GPS) fall under this category (Ashwini & Usha, 2014).
11
The implementation of the positioning techniques can be carried out in two ways; self-
positioning and remote positioning. When the receiver makes an appropriate
measurement from transmitters that are geographically distributed and uses this
measurement to determine its location, it is a self-positioning approach. Remote
positioning can be defined as when signals that are sent to and back from a set of
receivers are measured (Ashwini & Usha, 2014).
2.7. Location Technologies
The two means of location determination in cellular networking are referred to as
network-based location and cell ID look up. In cell ID approach, this method uses the
cell ID that the mobile device detects in its constant communications with the cellular
base stations (Statler, 2016).
The data for large number of cell IDs and their geographical information are collected
either by crowdsourcing or network operators. Some providers of cell ID location
services include Google, Rx Networks and LocationSmart (Statler, 2016). These
services take advantage of databases that contain the coordinates of more than forty
million cell sites that are deployed around the world. Using cell ID look-up, devices
that cannot be tracked using GPS or other means are locatable. However, a device must
have the special programming or the application installed for the information to be
looked up in a database (Statler, 2016).
The cell ID method is an active procedure from the standpoint of the device that is being
located. This means that the installed application or the embedded program built into
the OS of the device need to be constantly collecting and reporting its cell ID
information to the service provider or the provider of the application (Statler, 2016).
Network-based location method uses near real-time information within the network
about which cell sites are communicating or have communicated with the mobile device
that is to be located. In contrast with the cell ID method, network-based location method
does not require the mobile device to have any pre-installed application or embedded
programs to do any form of processing (Statler, 2016).
The mere functionality of the device, meaning being turned on and connected to the
network is enough for its location to be determined. Since location tracking in network-
based technology requires the mobile device only to play a passive role, the user’s
consent in allowing the service provider to use their location data especially for
commercial goals is an essential factor. Therefore, mobile device users are asked at the
12
point of making connection to the network whether they allow service providers to
access their location data (Statler, 2016).
A fairly new phenomenon in location-based services is beacon technology. Beacons
can be installed on surfaces such as walls and shelves as well as being embedded in
devices such as ATMs and vending machines or even products or packaging (Statler,
2016).
Some of what beacon technology was meant to be could be portrayed as a science-
fiction story like what the world saw in the film minority report with Tom Cruise. This
makes one to wonder about the possibilities of beacons not in terms of technology but
the evolution of the ecosystem surrounding this technology. Imagine shoppers being
welcomed by their name with messages containing products that are based on their taste
and demographics (Statler, 2016). Customers walking into coffee bars are taken in with
their name being called out offering “the usual” on their first visit and the ability to
make invisible payments without the need for physical money or money related items
like credit cards or as such. Smart displays at gas pumps start playing a favorite tune of
the customer as they exit their car to reach for the pump nozzle and just before that,
they see an infotainment (Statler, 2016). Arriving at the restaurant could be greeted by
concierges guiding you to your seat even though it is your first time there. Having got
access to a list of your dietary preferences, the restaurant provides you with relevant
menus and special offer of the day. Waiting in such circumstance will become if not
obsolete, a fading element. Airport travelers can be assisted by the ground crew in
finding the right gate for their luggage or the greeting family members could be
informed if there are inconveniences in the customs process when the information is
not necessarily shown on the information boards (Statler, 2016).
According to a report by NFC World in 2016, Gimbal has announced that they are
working on a beacon firmware that will turn any device with Bluetooth Low Energy to
act as a beacon. The main purpose of a beacon is to send signals that can be picked up
mobile devices in its surroundings (Statler, 2016). When the mobile app that is
programmed to receive beacon signals is present in a radius of inches to 50-75 meters
of the beacon, the app reacts and starts communication with the beacon. This
communication provides the opportunity to contact the user with an event, typically a
message such as an ad, offer or other form of information (Statler, 2016).
13
A beacon platform consists of hardware, software and mobile apps that are able to
recognize beacons’ presence in particular locations (Statler, 2016):
Beacons – Hardware includes one or more beacons that placed in several
locations in or around a particular area both indoors and outdoors. They can also
be embedded in displays, fixtures, PCs and other devices using a USB port
(Statler, 2016).
Integrated Apps – An app that is capable of recognizing beacons when in their
vicinity. The apps may include a retailer app or brand; also third-party apps such
as RetailMeNot or Shazam; social media apps can also function this way
(Statler, 2016).
Beacon Management Software – As the title suggests, the software allows the
beacon owner or its partner to monitor, supervise and configure the status of
beacons. Specification of unique identifiers and range as well as monitoring
battery life and so on are examples of use for management software (Statler,
2016).
Proximity Engagement Software – Allows the beacon owner or agency partner
to set up and manage campaigns. The campaign will the app what to do once
the beacon signal communication has been established. For instance, first-time
visitors can be welcomed by being offered a 25% discount offer; returning
guests to be offered a link to a loyalty program (Statler, 2016).
One of the major players in tech that got many investors excited about beacon
technology was Apple. iBeacon did not aim to define everything in the field at the time
for a solution but was in fact introduced with some gaps in between the strategy that
were the incentives for other businesses to try and fill those gaps and create value so
that an ecosystem suitable enough for the emergence beacon technology. While there
is a lot of attraction coming to iBeacon, the full impact of this technology is yet to be
understood (Statler, 2016). The extent of these implications go beyond the current
Bluetooth players, stream of ingenious ideas from software companies and advertisers
who are more than excited to take on the beacon technology as the tool of the future for
digital promotion of products and services. One of the uses of iBeacon is implementing
a proximity trigger for the purpose of marketing or other forms of automation. One key
element to pave the way for beacon technology to flourish is the OS supplier. While
they can open up doors to great opportunity for developers, they also possess the power
14
to shut out any unwanted factor from the ecosystem (Statler, 2016). They can cut off or
change services that developers relied on or start competing in the fields that they were
partners before. The vital role of the OS provider becomes clearer when we understand
that they need to create a technical equilibrium, accuracy, responsiveness, address
privacy issues, where to impose rules and regulations and where to allow for the
freedom of ideas to leave room for the community to contribute to innovation (Statler,
2016).
The Bluetooth beacon ecosystem is vast and large. Beacon costs are low and they offer
many options. Many Wi-Fi access providers are embracing Bluetooth beacon
technology as an added component for which they can charge enterprises. A company
like Cisco is taking advantage of beacons to incorporate this technology in their Wi-Fi
positioning systems to increase accuracy for a lower cost and also increase flexibility
as a result of higher density in number of access points (Statler, 2016). Aruba switched
to Bluetooth by completely dropping Wi-Fi as basis for their Meridian RTLS systems.
This was due to issues regarding the association of Wi-Fi networks in effective tracking
of location, cost, accuracy, flexibility, passive monitoring of Probe Requests issue and
a perception that Bluetooth has a better privacy story. With a large trusted sales force
and a powerful installed base, the reason for Wi-Fi service providers to aim for adopting
beacon technology into their business is very clear (Statler, 2016).
Another emerging and prominent form of location-aware technology is geofencing.
“Geofences are virtual perimeters that mark locations in the physical world”, says
Stephen Statler (Statler, 2016, p.309). Like beacons, they’re from the same family of
location-based communication but they do not demand the deployment of physical
hardware the beacons do (Statler, 2016). Geofencing has taken many forms in the years
and one could assume that it is as old as the mobile phones that we use every day. In
the early years, mobile operators used cell tower identifiers to determine the physical
location of the user (Statler, 2016).Early applications included SMS marketing
campaigns, tracking of inventory and even law enforcement. There are cases of the
release of cell tower data that either helped placing the suspect at the scene of the crime,
or prove their innocence of any wrong-doing. The next generation of geofencing will
take place on a mobile app level as it does not in fact concern the mobile operator
directly but rather uses a range of technologies and techniques to attract customers
(Statler, 2016). App-level geofencing should respect the three Ps of mobile: permission,
15
privacy and preference. By downloading the mobile app, users are showing willingness
to work with your company on some level; however, they would still need to allow for
location-based services to be turned on meaning the give permission (Statler, 2016).
The users are not meant to be tracked continuously in the background. This means that
previous location information while entering the geofencing zone is unknown. The
mobile device’s OS wakes the app up once it has breached the boundaries of the
geofenced location. With certain convenient reach to users once permission is granted,
it is easy to abuse the amount of content that users are to receive as ads. This means
that engagement principle should be strictly on users own terms. If users feel being
spammed especially with irrelevant content or that their data is being misused, they can
very easily revoke the authorization of location data and delete the app altogether
(Statlet, 2016).
Geofences can be a very effective tool in informing customers about promotions when
they enter a geofenced area. Through push notifications, geofencing allows for the
ability to remind the customers of the offers or coupons they had digitally received from
before so that they would pay a visit for a good reason (Statler, 2016).Physical
interactions at the right time and the right place with immediate value builds customer
loyalty and drives sales. An incentivized technique that can be implemented into this
value exchange procedure is called random intermittent reinforcement. It is the idea to
reward customers with treat once they are nearby (Statler, 2016).
People see this as a random act of kindness and therefore are encouraged to visit your
location more often. As there is no mention of this event in the app, the customers see
this as a random event and more likely do not expect it to happen all the time (Statler,
2016). This randomness is a perception of the customer when in fact it is a purposeful
attempt in marketing and advertisement by segmentation data and rules by the brand.
One example of such approach, is targeting a segment of high-value customers who
have not made any transactions in thirty days and are in the area right now. When you
bring personalization and CRM data into this in a scale single treat options for
customers, it can increase significant foot traffic in your location (Statler, 2016).
Apart from technological considerations for the application of Geofencing, there are
certain strategic business practices that need to be put in place in order to fully take
advantage of the technology (Statler, 2016). Geofences are completely virtual meaning
the y can be implemented anywhere in the world; as a result, a clever strategy would
16
placing them near the competitor areas. To know when and how often the loyal
customer visits the competitor sites can be a decisive factor in raising engagement levels
incredibly. However, sending uncalled push notifications to customers as they arrive in
competitor locations could counteract the purpose of the campaign (Statler, 2016). One
approach would be sending users these notifications later in the day or even after they
have left the location. A better approach would processing this as an analytic data and
then have it show up in the overall customer view. After segmentation of the customers
who visited competitive locations against your locations, you can calculate a loyalty
score of your own. This way the various campaigns that are run against competitors
based on location data can be adjusted and unselective customers can receive newly
modified offers and communications to change their previous shopping patterns
(Statler, 2016).
After connecting your CRM and loyalty system, it is important to remind the customers
of their point they have received and how long they have before they can use them. This
can also be carried out by using push notifications when they are at one of your locations
to encourage more purchases. If your brand app is capable of a check-in function, it is
useful to remind the users of this feature while they arrive at company location for their
social media followers to be informed (Statler, 2016). This enables a viral effect of
location awareness in social media. Incentivizing these interactions by for example
Easter eggs in the app such as social status or virtual currency make for a valid exchange
between the user and the app. Inciting a sense of accomplishment and enabling
customers to convey their status are two major emotional loyalty switches you can turn
on in your customers.
Asking customers about their experience after interacting with your Geofencing
campaigns is a necessity for surveying the effects. Later after customers have left your
company location, they can be asked in short multiple-choice questions about their
experience while on mobile; avoiding request to type in the answers would be wise.
The relationship between Geofencing and beacons can be described as a Russian doll.
The outer layers, the big dolls can be defined as Geofencing techniques while the inner
layers as small dolls could be defined as beacons (Statler, 2016).
When taken as a criteria for segmentation, regional campaigns can be implemented
using Geofencing on a larger scale and beacons on smaller one. For instance, if
Superbowl is taking place in Miami, geofencing can be used to encourage people in
17
Florida to buy tickets for the event that occurring in their region (Statler, 2016).
Residents of the West coast can be targeted with ads relevant to their region to be
informed about the events when beacons cannot be applicable as they’re far wide-
spread to be in contact for proximity marketing. A sample coexistence of geofencing
and beacons is as follows (Statler, 2016):
Figure 4. Geofencing mixed with beacon proximity areas
To acquire the right preparation for a successful location-based marketing campaign, it
is more likely that you will adopt both beacon technology and geofencing. Beacons can
be reliable technology used indoors for understanding granular proximities, once
Bluetooth is turned on. On the other hand, Geofences can be applied to much larger
areas using Wi-Fi (Statler, 2016). One great advantage of Geofences to beacons is that
competitor locations can be geofenced while beacons cannot provide the same service
in such locations (Statler, 2016). At the same time, beacons are the way to go, when
customers inside a building are to be targeted as geofencing works for areas 100 meters
large and above. Steel and concrete used in buildings also hinder the delivering of
messages using geofencing (Statler, 2016).
A brief summary of advantages and disadvantages of both technologies are shown
below:
Figure 5. A comparison between Geofencing and Beacon Technology
18
2.8. Location-based Marketing
After going through the technologies used for location-based services and having been
made familiarized with how these technologies are meant to be used, it is important to
assess the potential of location-based services in marketing and find examples of
successful marketing campaigns based on LBS. Location-based marketing is a an
efficient way to attract customers whether local or happening to be visiting out of area.
Not only this approach can be a way of new customer acquisition, but it is also to be
taken advantage of for building customer loyalty. The key to getting started in this field,
is to provide location-based social networking platforms with relevant, accurate
information so that potential customers can find your location of business (Hopkins &
Turner, 2012).
Once you’re able to have customers reach your doorstep or in other words, increase
foot-traffic, many tactics can be laid out to generate more revenue, build loyalty and
thus create a faithful sense of credible value exchange (Hopkins & Turner, 2012):
Promotional discounts and giveaways for initial check-in and repeat visits
Ongoing promotions where incremental discounts and special prizes are
awarded based on the number of customer check-ins
Targeted promotions through social network sites such as Twitter or Facebook
Running real-time promotional campaigns with customers physically visiting business
locations can drastically improve customer feedback and create a word-of-mouth type
of advertising by customers sharing their experiences via social media. One of the great
features of location-based marketing apart from enhancing customer experience, is
being able to target engaged customers at the time they’re more likely to make a
purchase (Hopkins & Turner, 2012).
In addition to the viral influence and promising convenience that location-based mobile
marketing brings to the table, the eco-friendly practices that it encourages simply by
putting forth a platform of digital nature, can offer tremendous cuts on use of non-
environment-friendly materials such as plastic used in cards or paper-made coupons
(Hopkins & Turner, 2012).
In order for a location-based marketing campaign to be successful and achieve the
expected results, it is important to set some guidelines that correspond to the current
stage of knowledge regarding LBM both from a consumer perspective and business
entity alike. As location-based mobile marketing is somewhat a new concept, it is
19
essential to address the consumer confusion about it (Hopkins & Turner, 2012). Yes
technologies like Bluetooth and Wi-Fi are long known by the public but a marketing
approach using these tools for approaching the users can raise doubt about the integrity
of your business intentions.
It is human nature to be afraid of something we do not understand. It is your concern
as the provider of the campaign to ensure secure practices while using data for
marketing goals. As not all people live in an actively commercial environment of
mobile services, the upholding of users’ permission is the first step in making the fact
understood that users will only be included in the process if they opt in for receiving
messages (Hopkins & Turner, 2012).
This also includes a clear instruction on how to opt out of these notifications to further
highlight the good intentions of the business respecting the rights of privacy; however,
having users’ permission does not mean to bombard them with marketing notifications.
There is a fine line between efficient marketing practices and low-value approaches in
high numbers (Hopkins & Turner, 2012).
This also means that users who confirm opting in for receiving notifications need to be
informed on what sort of content they will be provided with. Being clear and
straightforward with your interaction shows the customers exactly the emerging value
exchange that they’d be expecting and therefore look forward to (Hopkins & Turner,
2012).
While hinting on these issues, being faithful to the promises made at the opt-in phase
significantly increase loyalty. Running test campaigns with survey follow-ups is quite
essential for progressive improvement of the campaigns. These test campaigns can be
run on small budgets and provide certain incentives for the unbiased participants that
give their time for filling out the survey (Hopkins & Turner, 2012).
2.9. Case Studies
As new as location-based mobile marketing can be, there are a number of brands who
have already taken the notion of LBM into heart and applied it to their marketing
approach. In this section, we go over a few case studies of location-based mobile
marketing for some companies.
2.9.1. Dunkin’ Donuts
Physical location of a business is a very important decisive factor in driving foot traffic
your way but sometimes customers need a little push to come around your store and
20
explore the offers by taking a closer look.
Thubmvista, a mobile marketing and advertising company had the opportunity to work
with the popular donut company, Dunkin’ Donuts. Thumbvista, created a mobile
display ad campaign with help of geofencing by making use of users mobile devices
and their GPS function to draw more foot traffic to the location of the business and
therefore increase sales. By designing a geofencing program for a radius up to five
miles around the gas station where was the intended destination, the business was able
to identify the potential customers’ mobile devices within this distance.
Thumbvista also designed a landing page with a coupon embedded for users giving
them the option to share the coupon with friends via email or social media. It also
provided a link on the navigation map and an Apple passbook coupon for iPhone users
with a reminder trigger on for target location. Over a four day period, Dunkin’ Donut
had 164 people click the link to get the coupon in the target radius. From 12 p.m. to 2
p.m. were the busiest hours. The top day showed a Click-through-rate (CTR) of 76%.
This showed that a strong, creative location-based mobile marketing campaign with
proper ad incentives and embedded landing pages could drive sales higher than before
such a short period of time (Thumbvista, 2015).
2.9.2. Pizza Hut
Pizza Hut is a subsidiary of YUM! Brands, Inc. With more than 6,000 restaurants in the
United States and more than 5,000 restaurant locations in 94 other countries in the
world, Pizza Hut is the largest restaurant company. Pizza Hut was running oer-based
promotions that were raising sales but yet unable to build loyal customer relationships
particularly attracting customers who were price-sensitive. This led Pizza Hut to decide
for a campaign that was meant to build more customer loyalty and draw in more sales
at key restaurant hours.
Pizza Hut started a project with O2, a mobile carrier based in the UK, and a buying
agency called Starcom to create a location-based mobile marketing campaign via
ShopAlerts platform. The campaign targeted O2 subscribers and invited them to Pizza
Hut locations at key restaurant hours. As for the perimeters, half-mile geofences were
set up around nearly 340 Pizza Hut locations across the UK and SMS messages were
sent to users in the vicinity containing deals for happy hour, lunch buffets and special
offers like unlimited salads and kids eat free. Each SMS message was dynamically
21
created in real-time based on the location of the Pizza Hut closest to the subscriber
where the price of the deal was relative to the location.
During the course of the campaign, 301,000 test messages were sent to O2 subscribers.
The program ran straight for fifteen months on several media channels including
Mobile, TV, Radio, ATM and social media platforms like Facebook; above all, mobile
was the most successful platform with 142% more efficiency compared to other
channels in delivering sales and incremental revenue than the measured campaign
average. Mobile was 4.4 time more effective than TV ads and 2.6 times more successful
than online campaign (Placecast, 2012).
2.9.3. North American Auto Brand
A major North American auto brand set the objective of a location-based marketing
campaign by reaching specific device IDs sources into two categories of Polk and
Handraisers. The purpose of the campaign was to raise brand awareness around a
number of new car models and leasing information. The process of targeting by
Placecast was performed by using Placecast’s vast set of point-of-interest data to target
around four thousand auto-dealers in auto brands’ top 50 DMAs which included
competitors as well.
The users within the premises of the geofences around the car dealers were delivered
45 variations of rich media ads that were designed for giving information about several
car models, including leasing information. On-click, the users were redirected to
landing pages with detailed information about the deals.
For the category of Polk, users who had ad exposure were 1.2 times more likely to visit
the store in comparison with users who were never exposed to the ads. As for the
Handraisers category, the user exposure made for a 1.5 times likelihood of users visiting
the store in comparison with non-exposed users. Ad engagement for Polk category was
a rate of 1.17 times of the probability for users to visit the store and the Handraisers
category were more likely to visit the store by a rate of 2.12 times the users who never
engaged with the ads.
The overall results came to be a high rate of CTRs of over 1% for the brand’s own
dealer locations as well as its top competitors’ locations. The performance by day
showed the highest numbers on Wednesdays and from an app perspective, the
navigation apps did the best for the campaign (Placecast, 2013).
22
2.9.4. Flip Flop Shops
With more than 80 stores located across 10 countries in the world, Flip Flop Shops is a
global retail franchise that specializes in casual footwear. The case study by Turnstyle
took place one of the Flip Flops Shop outlets in Vaughan Mills. This mall has 250 stores
in a retail area of 104,000 square feet. The Franchisees, Preet Banipal and Larissa
Brumwell wanted to find a way to take advantage of the many number of people who’d
be walking past the store or standing around idly. After they found out that they could
use guest Wi-Fi to lure in customers in order to generate more leads and engage
potential customers, they decided to explore this opportunity further. On average,
visitors who connect to a venue’s Wi-Fi network stay on 23% longer than users who
are not connected; this provides the venues with a significant increase in sales
opportunity. Over a course of 8 months, Flip Flop Shops were able to see over 900
unique sign-ins in a single location which is equal to 3.8 new people joining their
marketing program every day.
After adding in-store signage to promote public Wi-Fi, that number increased to 5.1
new sign-ins per day which equates to over 1,800 per year. In order to convert the web
browsers into buyers, the store offered the users who were signing in with for the first
time with a 10% coupon off or a free bracelet for any purchase over 25 dollars. The
push notification for this offer was instantly shown to the users upon Wi-Fi
authentication and 16.8% of the users redeemed within an hour after receiving it. By
achieving these results, Banipal and Brumwell were able to offer free internet access in
exchange for valuable contact information from the users who connected to their
network and so increasing the growth of their email marketing list. Secondly, it enabled
them to drive more foot traffic into their store by immediate coupon offers and shopping
limit gifts for the users who successfully connected to their Wi-Fi network (Turnstyle,
2016).
2.9.5. Tokyo Smoke
Tokyo Smoke is a leading Canadian lifestyle brand. Founded by Alan Gertner in 2015,
Tokyo Smoke is a company that aims to use offer a unique experience to the
sophisticated smokers by elevating the state of mind of its customers. Two months after
opening the first Tokyo Smoke shop, Gertner decided to contact Turnstyle as he was
obsessed with data and convinced that there are in fact smarter ways to be a good
23
retailer. Alan wanted to please his customers by going over their expectations and
reward them for their loyalty.
By cooperating with Turnstyle, he was able to offer personalized digital coupons to his
customers based on their behavior and trigger points. In other words, he wanted to
replace traditional stamp card with a digital version that could be kept on customers’
mobile devices without the need to download an app. Each user that signed in, received
a custom branded message. The campaign consisted of three marketing strategies:
digital loyalty cards, first Wi-Fi sign-in and half-off the price of afternoon cup.
Customers who had previously signed into Tokyo Smoke’s Wi-Fi would receive a
digital stamp, thanking them for visiting and informing them about how many more
visits they could make until they received a free beverage. This strategy alone resulted
in a 33% rate in redemption of the digital coupons.
For first-timer sign-ins, the offer was instant reward for a $2 discount on any beverage;
a saving of up to 60%. This resulted in a redemption rate of 10%. The third strategy
was designed to target the morning visitors in order to encourage them to pay a visit in
the afternoon as well. This way, the customers who visited the shop in the morning
would receive an immediate coupon for a half-price beverage in the afternoon. This
strategy resulted in a redemption rate of 6.4% (Turnstyle, 2016).
2.9.6. Mondelez International & Couche-Tard
Mondelez International is a global leader in food and beverage industry. Mondelez
International took the initiative through their Shoppers Futures program, to bring start-
ups and retailers together to transform the consumer retail experience. As Sofia
Oliveria, Senior Brand Manager at Mondelez International put it: “Our goal was to
drive penetration with millennials and find unique and relevant ways to reach them with
our products in that moment of purchase for a high impulse category. We were curious
to know, how could we effectively grab their attention and then keep them engaged and
coming back?”
Together with convenience store powerhouse, Couche-Tard, who owns Mac’s and of
course the provider of location-based marketing solutions, Turnstyle, Mondelez
International was able to obtain valuable information about their customers, delivering
highly contextual offers and rewards to increase traffic and purchases. Turnstyle’s
cloud-based Wi-Fi marketing platform, powered by Cisco Merak access points, enabled
24
the two retail giants to successfully map, analyze and market to customers, generating
a considerable amount in revenue and customer loyalty.
The campaign was launched in a thirty-day period where 6 types of offers were
presented to the consumers depending on visit frequency. Logging on to the Wi-Fi
would reward the users immediately and simultaneously serve as an opt-in to future
communications with Mac’s new program. Later visits by customers who already
opted-in to the Wi-Fi would make the Cisco Merak Aps to trigger Turnstyle’s marketing
platform to deliver additional, personalized rewards and offers to customer’s mobile
devices; all without the need for a mobile application. The offers sent were designed to
modify customer behavior by encouraging repeat visits and therefore creating loyalty.
Signage outside the store was ensured to inform the customers of the opportunity to
score big on snacks. Control, however, was a decisive factor in the experiment. The
promotion was only offered in month of January, while December and February acted
as baselines.
The results of the campaign were interestingly surprising. Over 3,244 customers opted-
in for the program in January; a massive increase of 81% in sign-ins over the prior 30-
day period. Driven primarily by signage advertising the campaign. Turnstyle’s systems
showed that 30% of the customers used Facebook, creating a mine of valuable
demographic information for the marketing team. With additional information such as
age, gender and visitor frequency, Mac’s stores received vital information that enabled
them to tailor their future marketing campaign for their primary audience. The most
noteworthy results were coupon redemption rates and return on campaign. An
approximated amount of 14% of customers who received a coupon redeemed it during
the campaign. This is a much better accomplishment than the rate for email coupon
redemption which was about 2-3%. Despite the immediate rewards, the customers were
more likely to return after an average of three days to redeem their coupons and get
their free candy bar, meaning they’d retuned for a new visit. Overall the customers
returned to their local Mac’s 25% more than the time they had before the campaign.
By putting a strict focus on return, Turnstyle was able to demonstrate the full impact of
the 25% increase in customer loyalty. By the assumption that an average purchase of
$4, across Couche-Tard’s global chain, this approach has the potential to create
tremendous revenue through relatively low-cost investment in technology and customer
acquisition (Turnstyle, 2016).
25
3. Methodology
In this chapter, we discuss the methodology of the thesis and the philosophy of our approach
for conducting survey to collect the primary data. The author introduces the hypothesis for this
study with the help of literature review and aforementioned case studies.
3.1. The Research Question
“What are the implications of location-based advertising in mobile marketing?”
3.2. Literature Review
One important concept in marketing research is attitude (Kotler et al., 2011). The
purchase behavior is rooted in the consumer’s attitude toward a certain product.
Attitude can be defined as the enduring evaluation of favorable or unfavorable intent,
emotions and actions toward a particular object or idea (Kotler et al., 2011). With
attitude as a frame of mind for liking or disliking an object, be it a product or service,
its role in designing purchase behavior of the consumer is vital that it can economize
energy and thought and may be difficult to change (Kotler et al., 2011).
The research approach towards LBA in mobile marketing is mainly a technological
approach. So far, there have been only a few number of publications that have taken the
users’ attitude towards location-based advertising (researched in 9 publications with
surveys and one with a social science experiment)(Bauer & Strauss, 2016). Therefore,
the gap in the research spectrum in LBA for mobile marketing lies in a more user-
centric approach that identifies the concerns of users while interacting with location-
based mobile marketing and advertising (Bauer & Strauss, 2016).
Based on the literature review, this research aims to measure the influence of incentives
in encouraging users to have a positive attitude towards location-based mobile
marketing while privacy is considered as a potential barrier.
3.3. Delimitations
As a result of the research choices for this work, there are delimitations that influence
the scope of the research. Since the survey done by the author will be carried out on
online survey platforms, the research is aimed to analyze the users’ attitude towards
LBA rather than their behavior. The reason for this, is that measuring the users’
behavior in a real-life situation requires conducting social science experiments that
demand the proper funding and infrastructure. The focus will be on users as the
literature review specifically indicates that more user-centric on the subject is needed.
Also, upon landing on the questionnaire page, the respondents are asked to participate
in the survey only in case they possess a smartphone. The reason for this is that although
26
marketing campaigns based on location can be in fact carried out using Geofences, the
wholeness of the concept of location-based mobile marketing is most applicable on a
platform that properly corresponds to the most recent progress in technology.
3.4. Methodological Considerations
3.4.1. Definition of Research
“Research is a systematic process of collecting, analyzing, and interpreting
information (data) in order to increase our understanding of a phenomenon about
which we are interested or concerned. People often use a systematic approach when
they collect and interpret information to solve the small problems of daily living. Here,
however, we focus on formal research, research in which we intentionally set out to
enhance our understanding of a phenomenon and expect to communicate what we
discover to the larger scientific community.”(Leedy & Ormrod, 2010, p. 2).
3.4.2. Research Methodology
The purpose of this research is an exploratory study. Exploratory research is useful
when one tries to clarify one’s understanding a problem and to answer the question of
“what” rather than “how” or “why” (Saunders et al., 2009).
The method for research provides a comprehension of the research by presenting a
design backed by a theoretical explanation of its use and value (Saunders et al., 2009).
The better the understanding of the choices made, the clearer the path towards
answering the research question. In order to realize the basis of the data collection
method, we need to form a well-constructed definition of qualitative and quantitative
data (Saunders et al., 2009).
The qualitative approach to data collection is a process through which the researcher
becomes the explorer of the source of data on a more interactive level and thus enables
the researcher to obtain a certain level of detail from involvement in real experiences
(Saunders et al., 2009). Furthermore, a qualitative approach is one in which the
researcher who does the inquiry, often makes knowledge claims based majorly on
constructivist perspectives; In other words, multiple meanings of individual
experiences, meanings socially and historically constructed, with an intent to develop a
theory or pattern; or advocacy/participatory perspectives (i.e. political, issue-oriented,
collaborative or change-oriented) or both (Saunders et al., 2009). It also includes
strategies of inquiry such as narratives, phenomologies, ethnographies, grounded theory
27
studies and case studies. This way the researcher collects open-minded, emerging data
with the intent to create and process a theme for the data (Saunders et al., 2009).
On the other hand, a quantitative approach is one in which the investigator uses
primarily postpositivist claims to develop knowledge; as the following examples, cause
and effect thinking, reduction to specific variables and hypotheses and questions, use
of measurement and observations and the test of theories (Saunders et al., 2009).
Quantitative research applies strategies of inquiry such as experiments and surveys to
collect data on predetermined instruments that yield statistical data. In this scenario, the
inquirer sets out to achieve the completion of the meaning of a certain phenomenon
from the gathered views of the participants (Saunders et al., 2009).
As the basis for the design of the questionnaire is the secondary data, the mono method
is chosen to collect the necessary primary data. Saunders defines the mono method as
an approach to data collection in which a single data collection technique and
corresponding data analysis procedures are applied (Saunders et al., 2009, p. 151).
A survey is a strategy by which the researcher is able to collect quantitative data to
analyze the quantitative data using descriptive and inferential statistics (Saunders et al.,
2009). Additionally, survey can help collect the necessary data with the goal to suggest
possible relationships between variables in a model (Saunders et al., 2009). A limitation
to survey as a strategy to collect data is that it possibly cannot have a wide range as
other research strategies (Saunders et al., 2009).
This study is a cross-sectional research. A cross-sectional study sets out to study a
particular phenomenon at a particular time. The research done for academic courses are
constrained by time (Saunders et al., 2009).
3.4.3. Types of Data
The data collected in this research both include primary and secondary data; primary
data defined as the data that is mined and collected by the researcher is gathered by
conducting a questionnaire survey. As it is well-known, questionnaire is one of the most
used data collection techniques in the survey strategy. Since each respondent is asked
to provide answers to a same set of questions, it provides an opportunity to collect data
from a large sample prior to quantitative analysis. A questionnaire can be used either
for descriptive or exploratory research. A survey can provide the researcher with
primary data that can be used in descriptive or inferential statistics. (Saunders et al.,
2009).
28
The questionnaire is designed to collect data that is relevant for answering the research
question just as it is relevant to the literature review to create a possibility to compare
the empirical findings to that of the previously found data through secondary sources.
The questionnaire will be self-administrated carried out via internet. Self-administrated
questionnaire are usually completed by the respondents themselves. This method of
using self-administrated questionnaire is used to collect the quantitative data. The core
of the questionnaire is designed to measure the attitude of mobile users towards
acceptance of location-based advertising in mobile marketing based on the predictor
variable; incentivized ads. The other element of the questionnaire for the quantitative
research include the measurement of the use of location-based app by the respondents
and its relationship with their awareness of the function of the location services in
settings of their smartphone. This is also accompanied by a third scale of ordinal
variables measuring the frequency of the use of different connectivity types and a scale
to measure the ordinal preference of the mobile users for different incentives as
exchange value.
As for secondary data, quality daily newspaper can contain useful reports regarding
takeover bids or companies’ share prices. Government departments publish reports with
official statistics that cover social, demographic and economic topics. Trade
organizations collect data from their members that are aggregated subsequently and
published. Secondary data are not only useful in laying the outline for collection of the
primary data, but they also provide a criteria by which the primary data findings can be
compared and made sense of (Saunders et al., 2009). The sources of secondary data that
are chosen for this thesis include case studies, articles published online, journals and
surveys done either by researchers or companies that have been in fact engaged in
location-based marketing practices as mentioned in the frame of reference. The
secondary data are the backbone of the empirical section of this research.
3.4.4 Sampling
The sampling technique chosen for this thesis based on the research question is a
probability sampling. Probability sampling is more often associated with survey-based
research strategies where the researcher needs to make inferences from the sample
about a population to answer the research question (Saunders et al., 2009).
As for the type of probability sampling technique, the author has chosen the simple
random sampling as it is used when the researcher has access to a database of potential
29
responders. Such sampling approach reduces the chance of human bias (Bryman &
Bell, 2012).
Danielsoperonline statistic calculator suggests that the author needs 67 respondents for
regression analysis with significant results (Danielsoper, 2017). Regression analysis
forms the basis for the analysis of the empirical primary data as the author aims to find
the strength of relationship between variables, aiming to achieve an argument for a
cause and effect relationship based on the nature of the research. More will be discussed
about the regression analysis as the variables collected are not numerical in nature but
categorical and thus analyzed using rather generalizations of the multiple regression
method (Laerd Statistics, 2013).
3.4.5. Credibility of Findings
For a research to be credible there are two criteria by which the evaluation of the
empirical findings can be performed; reliability and validity (Saunders et al., 2009).
Reliability can be referred to as the extent to which the data collection techniques and
analysis procedures applied by the researcher will produce consistent findings. The
survey designed here is to ensure that the data that are mined using similar techniques
and methods are to arise if these techniques and methods are applied on other occasions
and other researchers.
As for the validity, it can be described as the connection that the test as a tool has with
that which is the findings; in other words, is the researcher measuring what they are
intended to measure. For this, the author has tailored the questionnaire in a way the
clear-cut form of wording and placement of the questions require little effort from the
respondents’ part to put in in order to answer the questions.
3.5. Conceptual Framework
In order to make logical inferences from the research findings, the author has developed
a framework based on the literature review and the practical case study findings of
location-based mobile marketing companies. As the questionnaire is considered to be
the core from which inferences to be made, we dive deep into the concepts and theories
that suggest the best guidelines for designing the questionnaire for quantitative data.
The questionnaire designed for collection of the quantitative data aims to receive data
on the following main aspects:
1. Users’ awareness of location services function.
30
2. The popularity of most used connectivity types; namely Wi-Fi, cellular data,
Bluetooth and near-field communication (NFC).
3. The preferred types of value in exchange for location data; namely cash rewards,
store credit, coupons and loyalty points.
4. The role of incentivized ads that provide incentives such as offers and coupons in
return for location data.
The popularity of location apps is measured as an initial step to realize whether the
sharing of location data is already a resolved issue for the value that merely the function
of an app provides. The frequency of most used connectivity technologies is assessed
to achieve a better understanding of how marketers can focus on certain types of
technology that are already well-known and have proven their worth in order to provide
the channel through which the users can be approached.
In general, users’ privacy concerns are higher with regards to location-based advertising
whereas the perceived benefits and their willingness to use LBA are relatively low
(Magedanz & Simoes 2009). As such, the section of the questionnaire about value
exchange sets out to evaluate the desire of users for receiving an exchange value and
measure their openness to receive types of value categorized as cash rewards, store
credit, coupons and loyalty points. In this manner, respondents are aware that apart from
the value defined as the mere functionality of the apps, there are other types of value
worth their data if they are willing to consciously share location data with marketers
and advertisers.
Consumers tend to take a critical view of sharing their location data with marketers and
advertisers while they are agree to do so with family and friends. However, if the user
is given the ability to specify when and for how long their location is to be shared with
advertisers, their willingness to do so increases in comparison with a scenario in which
they don’t have the power to do that (Kelley et la. 2011).
As for the part of the questionnaire aimed at measuring the desire of users to receive
incentives in ads for their location data, respondents are questioned about the
convenience and the potential encouragement underlying the need for location-based
mobile advertisements. According to Bauer, three of the reviewed publications (Ho
2012; Cremonese et la. 2010; Tussyadiag 2012) suggest that discount tickets are more
appreciated than informative advertisements (Bauer & Strauss, 2016). It is argued that
the perceived benefit is the main driver for mobile advertising from the customers’
31
perspective. Therefore, advertisers should consider the relevance and the benefit of an
advertisement for the targeted audience and only provide meaningful ads based on the
data that are collected (Haddadi et al. 2010; Merisavo et al. 2007; Conti et al. 2012;
Unni and Harmon 2007). Based on this information, the section about attitude towards
incentives is mainly focused on offers and coupons rather than cash rewards and store
credit as this is a more realistic, cost-friendly approach for advertisers to run location-
based marketing campaigns. The poll on types of exchange value as previously
mentioned, is to introduce new grounds on which advertisers can drive even more
revenue based on value that includes money as the main marketing factor.
Based on this information, namely the fourth aspect of the research, the following
hypothesis is to be tested:
Incentivized location-based mobile adverts positively affect the attitude of consumers
who use location apps towards mobile marketing.
On an important note, acceptance of location-based mobile marketing is synonymous
with accept value in the analysis.
The forming of a hypothesis is an approach through which the research question can be
answered. While using inferential statistics to analyze the data, the author sets out to
choose the most proper techniques that make sense in statistical terms. This way the
results of the survey can be more reliable from an analysis point of view.
In the hypothesis we assume that the users whose acceptance of location-based mobile
marketing is at question, are already in fact using location apps actively. These location
apps can range from certain navigation apps to apps that can use navigation as a side
function to provide a main service like food delivery where the main function is
ordering food.
While it is explained how the awareness of the mobile users is meant to be measured
by the first aspect of the research, this an essential step in making sure that the
hypothesis testing is based on the cause and effect analysis where respondents are
actually interacting with location data even if this interaction is limited to the natural
function of the apps that use location data.
3.5.1. Testing the Hypothesis
Choosing the suitable statistical tests is a vital step to enable the researcher to correctly
assess the hypothesis and make the right interpretation of the data.
32
To test the hypothesis, a binary logistic regression is performed to evaluate the effect
of incentives on the acceptance of location-based mobile advertising. A binary logistic
regression predicts the possibility that an observation falls into one of two categories of
a dichotomous variable based on one or more independent (predictor) variables that can
be either continuous or categorical (Laerd Statistics, 2013).
In this regression, the dependent variable is the acceptance of location-based mobile
marketing which is dichotomous having only two values namely yes or no. As for the
independent or predictor variable, we have incentives which is measured as a
categorical (ordinal) variable. The regression is preceded by a correlation analysis using
the Kendall’s rank correlation coefficient. The reason to choose this test over
Spearman’s rank correlation coefficient, even though they’re both used for ranked data,
is that Kendall’s tau is a better choice for tied data as it is the case for this study
(Saunders et al., 2009). The reason for this analysis is to measure the correlation
between the independent variable and the dependent variable to shed more light on the
correlation as well as the possible causation resulting in the regression analysis.
Cronbach’s Alpha test is performed to check for reliability of data.
33
4. Empirical Findings and Analysis
In this chapter, we take an in-depth look into the findings of the empirical research and the
analysis of the collected data. The author aims to achieve a proper understanding of the
findings in order to form an argument to be presented in the discussion.
4.1. Statistical Considerations
In this study, both descriptive and inferential statistics are applied in order to deliver a
comprehensive understanding of the collected data. The first three aspects of the survey,
users’ awareness of location services function, popular connectivity types and types of
value in exchange for location data are illustrated using tables to provide an overall
understanding of the respondents’ position on these topics amongst mobile users.
As for the user’s awareness of the functionality of location services, we put forth the
number of respondents who indicated if they use location services option in the settings
of their smartphone against the question on whether they actually use location apps or
not.
To represent the value that occurs more frequently the mode is most suitable choice.
For descriptive data, the mode is the only measure of central tendency that a reasonable
interpretation of which can be made (Saunders et al., 2009). The mode as the measure
of central tendency is used to find the most occurring factor in connectivity types and
types of exchange value. As these data are categorical and not numerical, we avoid the
calculation of data dispersion (Saunders et al., 2009).
For the fourth aspect of the research, inferential statistics are applied to enable the
author to make predictions based on the analysis of the data.
As for the fourth aspect, the hypothesis, the author will conduct a binary logistic
regression. Binary logistic regression predicts the probability that an observed
evaluation falls into the two categories of a dichotomous dependent variable based on
one or more independent variables that can be categorical or numerical (Laerd
Statistics, 2013). The suggested conceptual hypothesis is evaluated using the binary
logistic regression as the dependent variable is defined as categorical (nominal) within
the range of yes or no to the acceptance of location-based mobile marketing and the
independent variable is defined as the median values of the Likert scale measuring
respondents’ view on incentives. These independent variables are known to be
categorical (ordinal). The Likert scales consisted of 6 questions each, assessing the
responses from 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree) and 5 (strongly
34
agree). As the nature of Likert items in the scale is a categorical (ordinal) type of data,
the median of the data rather than the mean is used to merge the outcome of each scale.
The response rate of the survey is 93.05 %. From a total of 72 potential respondents, a
number of 67 respondents participated in the survey.
4.2. Descriptive Analysis
At this stage of the analysis, we will present the information gathered through the
survey to indicate the popularity and use of connectivity types and forms of incentives
in order to shape the guidelines based on this study for the application of location-based
mobile marketing. To assess the aforementioned items, the mode, as a measure of
central tendency is used to deliver the most frequent occurring values.
4.2.1. User’s Awareness
In order to assess the respondents’ awareness of the location services function and its
obvious effect on functionality of location apps, the two questions with different focus
were asked to measure this awareness. The respondents were asked firstly if they have
location services turned on in the settings of their smartphone followed by whether they
use location apps.
Here are the results for the two questions:
Table 1. Using Location Services Function
As shown in the table, 73% of the respondents have answered with yes for the first
question regarding settings on their smartphone and a rounded 27% with no, meaning
they have turned off the location services in the settings.
Table 2. Using Location Apps
In table 2, we can see that 95.5% of the respondents have replied with yes to whether
or not they use location apps and a small number, only 4.5%, have answered that they
do not in fact use any location apps.
35
4.2.2. Connectivity Types
In this section of the questionnaire, the respondents are asked to mark how often they
use the connectivity types. The categories are: Wi-Fi, Bluetooth, Cellular Data and
Near-field Communication (NFC). The options are coded as 1 (most of the time), 2
(sometimes), 3 (rarely) and 4 (almost never).
The findings can be summarized as the table below:
Table 3. Connectivity Types Ranked by Popularity
As we can see from the results, the mode for Wi-Fi and cellular data can be categorized
as most of the time. In order to grasp a better understanding of the usage of these two
connectivity types we can refer to their individual tables:
Table 4. Wi-Fi Ranking
Table 5. Cellular Data Ranking
By comparing the two, we can observe that even though the mode for both technologies
represent the value of 1 (most of the time), the number of respondents who use Wi-Fi
stands at 57 which is 24 users more than cellular data. This shows the more tendency
of users towards Wi-Fi rather than cellular data.
36
The individual tables of Bluetooth and NFC are as follows:
Table 6. Bluetooth Ranking
Table 7. Near-field Communications Ranking
As shown in the tables above, the data for the usage of Bluetooth technology is
representing two modes of values 2 (sometimes) and 3 (rarely). The use of NFC stands
majorly at lowest frequency which is 4 (almost never).
4.2.3. Incentive Types
In this step, we will present the data collected for the popularity of four different
incentives as encouraging elements for sharing of location data. The incentives are
categorized as Store Credit, Loyalty Points, Cash Rewards and Coupons. The ranking
evaluation is categorized as 1 (first), 2 (second), 3 (third) and 4 (last). To implement a
sense of order respondents were informed that this grid is meant to rank from the most
interesting incentive to the least interesting one.
The overall results can be shown in the following table:
Table 8. Incentive Types Ranked by Popularity
As seen in the table, cash rewards stand as the most popular form of incentive followed
by store credit at the second place. We can see loyalty points and coupons both at the
last rank so in order to clarify the detailed difference, the individual tables are as
follows:
37
Table 9. Store Credit Ranking
Table 10. Loyalty Points Ranking
Table 11. Cash Rewards Ranking
Table 12. Coupons Ranking
While loyalty points and coupons stand at the bottom of the interest, we can see that
loyalty points have only one difference value between being the third interesting choice,
standing at 24, and the fourth, standing at 25, which is presented as the mode for this
category. The coupons however have a clearer tendency towards being the last choice
of the respondents.
4.3. Inferential Analysis
As to try and prove the hypothesis, the author will undertake inferential statistics to test
it in order to grasp a better understanding of the collected data and make predictions
based on this analysis.
38
4.3.1. Cronbach’s Alpha Test
To begin the processing of data for regression analysis, we have to first make sure that
the data collected through the Likert scale is reliable. In order to do so, Cronbach’s
Alpha test is performed to find level of reliability for the data.
This is the initial alpha level for the incentives scale:
Table 13. Reliability Statistics
The alpha value for the incentives scale is at the low end of the questionable level
(0.7>α>0.6). To improve the result we examine the initial total statistics table:
Table 14. Item-Total Statistics- Initial – Incentives
As we can see from the table, removing question no. 03, will provide a large
improvement for the scale. In order to better make the alpha closer to 0.7 value, the
following improvement table is presented:
Table 15. Item-Total Statistics- Final – Incentives
By examining the table, we understand that removing question no. 02 will provide an
alpha value of 0.735 which is perfectly in the range of the acceptance range
(0.8>α>0.7).
39
4.3.2. Kendall’s Ranked Correlation Coefficient
Now that we have made sure that the scale data are reliable, we will continue to the
next step by going through the binary logistic regression analysis. Before we run the
regression we go over the results from the correlation analysis.
The Kendall’s ranked correlation coefficient results can be seen here:
Table 16. Kendall’s Ranked Correlation Coefficient
The table for Kendall’s ranked correlation coefficient shows that there is a weakly
positive but significant correlation between the independent variable, incentives, and
the dependent variable, accept value. The significance of this correlation stands at 0.037
(p-value<0.05) and its positive strength level at 0.227. From this analysis we can
understand that by an increase in the incentives initiative, the attitude of the users tend
to lean also onto the positive side which is sharing location data for incentives through
mobile ads.
4.3.3. Binary Logistic Regression
While correlation is defined as when a change in one variable is accompanied by a
change in another variable without knowing which one cause the change in other, a
cause and effect relationship can be realized under the notion that a change in one or
more independent variables causes a change in the dependent variable; this is what we
come to know as coefficient of determination or regression coefficient (Saunders et al.,
2009).
As the first step it is important to note that the output for a logistic regression comes in
two blocks; block 0 ad block 1. Block 0 gives the output where the model is decided
without the predictor variable in effect. Block 1, on the other hand, includes the
independent variable which is needed for the cause and effect analysis.
Since Block 0 output is not the goal of this regression analysis, we will only take the
results for the classification table in Block 0 in order to compare the correct
classification rate to that of the corresponding table in Block 1 to check for an increase.
40
At first we have a look at the omnibus tests of model coefficients:
Table 17. Omnibus Tests of Model Coefficient
This table shows that the new model (Block 1) with the predictor variable (incentive)
included, is in fact significantly a better model than the baseline model where incentive
is not included. We are interested in the model row values as step and block values are
only meaningful if our regression were a hierarchical logistic regression.
Next the figure for model summary:
Table 18. Model Summary
The model summary presents us with pseudo-R2 values for the logistic regression. In
standard regression, the coefficient of determination (R2) is an indication of how much
of the variation in the dependent variable is explained by the model. The pseudo values
are instead used in the logistic regression as the conventional R2 values cannot be
calculated. From the values in the model summary we understand that 6.5 to 8.7 percent
of the variation in the dependent variable can be explained by the model in block 1.
Although these values are interpreted the same way as standard regression we refrain
from emphasizing on the results.
The Hosmer and Lemeshow test presents the goodness of the fit:
Table 19. Hosmer and Lemeshow Test
As the significance level shows, the model is a good fit for the data. We want this value
to be more than 0.05.
Next up we compare the two classification tables from Block 0 and Block 1. This shows
us how the model changed from the baseline by incorporating the predictor variable:
41
Table 20. Classification Table – Block 0
As the table from Block 0 shows, the correct classification rate of the data is at 56.7%.
Here we see the classification table from Block 1:
Table 21. Classification Table – Block 1
The table in figure 27 shows that the correct classification rate in the model including
the independent variable has increased to 64.2% and this means that the model has
improved in comparison with the baseline model in Block 0.
Finally at the last step of logistic regression analysis, we go over the significance and
strength of the predictor variable, incentives, with the dependent variable, accept value,
in our model. Here is the table for variables in the equation:
Table 22. Variables in the Equation
The table shows that incentives have a significant influence (p-value<0.05) on the
acceptance of location-based mobile marketing. The important indicator of the effect
of a change in incentives is exponential of B coefficient, Exp(B). As shown, the Exp(B)
value for incentives is at 1.841. Any value above 1.0 is considered to have a positive
effect on the dependent variable. This means that a one unit increase in incentives is
followed by 1.8 unit increase in the acceptance of location-based mobile marketing
which is towards the positive attitude of the users towards such service.
42
5. Discussion
In this chapter, we will go over the discussion in order to interpret the results regarding the
research question based on the analysis of the data. The author will address the truth criteria
and the limitations of the study as well.
5.1. Explaining the Findings
In this section we explain the findings of the research and its analysis as to try and find
out what the survey has achieved in order to draw conclusions later. In order to present
the information in a neat manner, we will follow the order of the four aspects of the
research mentioned in the methodology; users’ awareness, popularity of connectivity
types, popularity of incentives types and the cause and effect analysis of incentives as
predictor on acceptance of location-based mobile marketing.
As the results for the users’ awareness suggest, there is somewhat an important
misinformation amongst the users of this study about the function of location services
settings and the fact that most of these users actually use location apps. While some
apps might provide some sort of functionality when the location services settings is
turned off on a smartphone, many others do not function at all. This shows that the
majority of respondents are unaware of this fact and tend to be skeptic about the notion
of sharing location data through location services in the settings of their smartphone but
at the same time, use location apps on daily basis. The reason for this somewhat
contradicting attitude towards functionality of location data can be rooted in privacy as
a major concern for the users. As we know, the sharing of location by switching on the
location services option is completely under control of the users and it is not something
that can be forced upon them. One cannot claim that is unbeknownst to them either.
These are indications that mobile users feel that whatever usage of their location data
perceived as something that they need to be aware of and provide permission for, is
conceived as a threat to their privacy if not lawfully permitted. If we were to provide a
solution for such beliefs, it is most likely better to provide the users with an option to
opt out of any marketing programs that advertisers want to offer. Also, a limited period
of time can be convincing and less straight-forward as to give users a sense of control
so that they feel safe and in command of the data leaving traces behind for marketers
and advertisers to find. As we discussed in the case studies, mobile users are likely not
to shy away from using a service that provides free Wi-Fi. Raising the interest of mobile
users in such a way can be a cheap and cost-friendly way of letting the consumers know
43
that the value they are receiving can come in various forms and in a sequential format
where they’re rewarded almost every step of the way.
The matrix for the popularity of connectivity types is not only a good evaluation of the
approach for the infrastructure of location-based marketing campaigns but also an
indicator of how these platforms as infrastructure can be used as incentives themselves.
This is also backed by the case studies included in this research where free Wi-Fi as an
example, is used as an initial encouraging factor to draw foot traffic near the business
hub and from there, offer the incentives that directly focus on the purchase intention of
the customers. As we found out, Wi-Fi is the most popular form of connectivity
amongst the respondents. This approves the idea that Wi-Fi can be a preliminary
element in driving the message of location ads on mobile in order to increase foot
traffic. As cellular data is seen as type of connectivity that is used while Wi-Fi access
is not present, this is yet another reason for Wi-Fi to steal the show.
While Bluetooth and Near-field Communication (NFC) stand at third and fourth
rankings of usability, one cannot deny that these technologies remain relevant.
Bluetooth technology has been long used for file transfer and remains as the choice for
the wireless speakers and headphones that take the hassle of wiring out of the picture.
NFC technology stays at the bottom as we are yet to apply this form of connectivity in
our daily lives. With the emergence of iPhone 8 and iPhone X, Apple Pay tends to play
a more vital role in revolutionizing the digital payments through the use of Near-field
Communication (NFC) and its more security via the integration Face ID technology
(TechChurch, 2017). These are all signs that by undertaking a well-introduced
approach, Bluetooth and NFC technologies can finally play a role as important as more
popular technologies like Wi-Fi. Hopkins believes that the excitement about NFC has
raised since mostly because Google has joined a cadre of wireless carriers, banks and
credit card companies to lay out the structure for use of NFC for mobile payments in
the United States (Hopkins & Turner, 2012, p. 45). He adds that with NFC we will soon
see that although mobile users are scanning QR codes to connect to promotions and
web pages using their mobile phones, as more and more people start adopting this
technology, waving phones over NFC chip embedded posters, point-of-purchase
materials and check-out payment processors can be seen a new way to transfer data
from smartphones to electronic chip capable of near-field communication connection
(Hopkins & Turner, 2012, p. 46)
44
The popularity of Wi-Fi can be regarded as the most convenient way of using the
internet especially at someone else’s cost at times. With cellular data, this is rarely the
case unless the phone that is providing the internet is doing so through Wi-Fi by sending
and receiving data via cellular data for internet access. These different forms of
connectivity offer unique features in their practicality and that is why each if not all of
them can be used in different settings tailored for the needs of marketers and advertisers
just as much as the consumers themselves.
The incentives ranking matrix provided us with some information on what could the
demanded value for location data from the side of the users in this study. Cash rewards
as the first choice of interest followed by store credit as the second, indicate that while
other value exchange ideas such as loyalty points and coupons seem more realistic, it
is almost impossible to neglect the concept of money as value. The underlying reason
for this is probably that with money-based incentives, the marketers do not eliminate
the element of choice in the eyes of the consumer. This element of choice is not meant
as the type of incentive but the very incentive that offers no restriction on what the
consumer should or could expect as an exchange for their location data. However, as
we saw in the case studies mentioned, money-based incentives were not included in
campaigns at all. The focus was on offers and coupons as reduction in prices acting as
incentives and they were successful all the same. This could mean that money-based
incentives can be run on one-time marketing campaigns that not only draw a lot of
attention as being rare but also appeal to the sense of freedom of choice that tends to
linger in consumers’ mind from time to time. While case studies suggest a more
practical approach which is cost-friendly as well, the survey here suggests a more exotic
approach that is actually more popular backed by data. With money as an incentive,
marketers and advertisers can reach out to many more users who are interested in
receiving this kind of value because it is simply the commodity that speaks to everyone
due to its flexibility of choice. While money as an incentive can be seen as not so cost-
friendly from a business point of view, coupons can offer the same form of value only
appearing in a different way. What this means is that offers that cut prices in order to
attract potential customers are in fact saving the consumers money. However, with
coupons and offers acting a money incentives, it is also important to note that these
incentives are still bound by the nature of they are offering. A 20% discount on a cup
of coffee for example could be appealing to many people but not so much as for people
45
who are not simply looking for a cup of coffee. This brings up the topic of reaching the
consumers at the right time and the place as previously mentioned in the case studies.
While certain offers particularly for food and beverages can be more plausible and
efficient, the nature of the offer or he incentive as we’d call it, should be relevant and
appealing to the audience the advertiser is trying to reach. Such implications in the
practical case studies we have seen tend to use the need based on dimension of time
and space as proper indicators of when and where to reach the potential customers. By
neglecting these issues, valuable offers can ignored from consumers simply because of
inconvenience of the process of reaching the businesses that are offering the incentives.
Overall the driving of foot traffic to businesses using location-based mobile marketing
ads should keep at practicing forms of strategies that target the users at the most
opportune time to ensure the purchase intention of mobile users based on their location
and the data that suggests at what time of the day these purchases mostly occur. This
could be an indication of running various campaigns that do target users at different
times in order to gather the necessary data on the best time windows that marketers and
advertisers can reach for grabbing the attention of the consumers and generate high
conversion rates.
And finally the testing of the hypothesis that incentives positively affect the attitude of
users towards location-based mobile marketing. Before discussing the results of the
Likert scale analysis for incentives, we review the Likert items so that the measurable
factors are well explained.
By designing the incentives scale, many aspects of why and how the users could see
and use location-based ads were measured. With regards to the questions that passed
the Cronbach’s Alpha test to increase the reliability of the scale data, we can see
questions that evaluate the idea of incentives as a decisive factor for mobile users to
feel less worried about sharing their location data and also to realize how these
incentives are meant to be presented to the users so that the convenience factor would
not fade away as a constant structure through which these encouraging elements are to
be delivered.
As the incentives scale items show, they are designed to measure various aspects of
incentives in a manner that individualism of the users, permission as a privacy control
factor and convenience of use are all included. Such an approach enables the scale to
46
directly focus on incentives as a pleasant motivator and not a marketing and advertising
tool of bombardment; a bombardment with ads.
Question one, asking about relevance of ads to customers’ needs, concentrates on
purposefulness of adverts so that the respondents can decide whether or not the
receiving of relevant ads is a factor in being tolerant of ads that target them on daily
basis. This is a vital step in the scale as it is a hidden driver in providing answer for the
doubtful mind of users who tend to be pessimistic about the advertising business.
Question four, asking about how willing the users are to receive coupons that can be
redeemed after a certain amount of time, is leaning on the idea of how little flexibility
can be put in the deadline for using the coupons so that the lack of need from the
consumers’ side is met with patience and a choice based on time. When designing this
particular item, the author tried to convey the idea of how digital coupons are not meant
to be lost; not by means of losing them because of their physical state but also keeping
them in a device that provides comfort of storage on a virtual platform.
Question five, asking about the time-efficiency of coupons and offers that reach the
users first, is devised to grasp the idea of users about the factor of time as fleeting
resource that can be optimized. As previously mentioned, the technologies that enable
location ads deliverance based on premises over certain vicinities can reach mobile
users with ads that inform them about the deals that are being offered around from the
shops. These strategies can be integrated with traceable search patterns left by mobile
users who have already agreed to the terms of service from advertisers so that targeted
ads can become more appealing than before by factoring in the real-time delivery of
offers and deals.
Question six, asking about receiving ads via one app rather than several apps from
several brands, is meant to measure the desire of mobile users towards their idea of the
channel to receive mobile ads and whether or not, based on findings of this study, the
brands can in fact benefit from merging the channel for incentives ads so that a unified
channel can bring comfort and ease of use based on the limited storage that mobile users
might have.
As the cause and effect analysis showed, incentives as an encouraging factor can
significantly affect the users’ attitude towards location-based mobile marketing in a
positive way. This is to show that the barrier between the users and marketers’ access
to location data majorly known as concerns of privacy can be overcome when a valid
47
form of value is offered to the consumers. While the idea of location-based mobile
marketing is yet to be implemented in large-scale operations, the mobile users are
already quite familiar with the idea of incentivized ads. They interact with them in
gaming apps as an example where the user is encouraged to watch a video where they
receive in-game currency as a reward. The challenge however, lies in normalizing the
location sharing process where not only incentives are offered but the consumers are
also met with convenience as a natural result of technological advances. The time of
looking for things has long been replaced with things we are searching for to find us
instead and this process needs to find its way through location-based advertising to
convince the mobile users that efficiency and practicality can be an additional value for
when their location data is treated as a commodity. The focus of mobile marketers and
advertisers should be set on meeting the needs of consumers based on an appropriate
approach at the right place and the right time. This way, the stigma with marketing and
advertising can be newly defined as a force for the good of mobile users; never meaning
to overwhelm and never meaning to divulge location information with unwanted
parties.
5.2. Truth Criteria
In this part, we will go through the reliability, validity and generalizability of the study
in order to prove that this research meets the truth criteria.
Saunders defines reliability as the extent to which data collection techniques and
analysis procedures will yield consistent findings (Saunders et al., 2009). To measure
the reliability of the data, the author has conducted a Cronbach’s Alpha test to ensure
that the collected data meets the acceptable alpha value of more than 0.7 so that the
reliability of findings can be backed by proper analysis tools.
In order to ensure the validity of the findings, the questionnaire was designed in a way
that the clear wording of the questions would increase the understanding of the
respondents of the questions and the notion of location-based mobile marketing to
improve the validity of the survey (Saunders et al., 2009). As an additional insurance,
the questionnaire was initially run with a number of preliminary respondents to enhance
the simplicity and focus of the questions. Saunders says that validity is also concerned
with finding out that the results are really about what they appear to be about, he
suggests a cause and effect analysis to realize the relationship between the two variables
(Saunders et al., 2009). This is why the author has undertaken a regression analysis.
48
The binary logistic regression was chosen to fit perfectly with the conceptual
framework and the nature of the survey.
While according to Saunders the research results of a case study at an organizations are
safer not to be generalized to the whole population and should be limited to the
framework of the particular bindings of the company or organization, he also suggests
that the generalizability of a study which has been conducted based on sample survey,
can be backed by the secondary data that has achieved the similar results (Saunders et
al., 2009). In this case, the author has covered the role of incentives on users’ positive
attitude towards location-based mobile marketing in the secondary data provided by
Bauer as literature review.
5.3. Limitations
As mentioned before, according to Bauer, there must be clear distinction between
mobile users’ attitude and their behavior. This means, that the behavior of the
consumers while being assessed in a real-life condition while dealing with location-
based advertising could in fact differ of their opinion or attitude towards LBA. Such
distinction makes it clear that although studies like this are needed to evaluate the
awareness and attitude, they do not however include the real-life aspect of the users’
experience while interacting with location-based mobile marketing. In order to conduct
a study within the dimensions of experimental research not only the researchers need
the proper funding but also a randomized sampling which happens naturally within the
setting of a hub of business as the physical vicinity for the research. It is only in these
conditions where a cause and effect analysis can be performed with the option to
completely remove or include a predictor variable in order to observe its effects on a
dependent variable such as purchase intention of the consumers in a mobile
environment.
Another limitation of this study could be defined as its generalization. However this is
not because of the sample size or the sample choosing technique but rather the limited
number of studies such as this thesis in academic and practical spectrum of location-
based mobile marketing. Although a few publications have undertaken such research
in this subject, the consensus approves of the generalization of the findings of this study
as mentioned in the truth criteria nonetheless.
49
6. Conclusion and Recommendations
In conclusion and recommendation, the author draws conclusions from the discussion and
makes recommendations based on these conclusions for the future studies with regards to the
current research stage in location-based mobile marketing.
6.1. Conclusion
After discussing the research findings, it is worth drawing conclusion for implications
of location-based mobile marketing in the near future.
As we have seen from the results and the explanation of findings, it is important to
highlight the role of incentives in the constructive communication that marketers and
advertisers try to establish with the mobile users. This means, not only incentives are
the driver for the end goal of generation of revenue and foot traffic but also the decisive
factor in convincing the consumers to share their location data willingly. Acquiring
permission is a very vital step in which the users are conveyed a sense of control so that
the freedom of choice as a fading idea in the consumers’ perspective can be revived as
a tool of power and control within the hands of the customer. It implies that without a
credible form of service that promises both value and of recognition of concerns form
users’ side, it is quite the challenge to mine the data that are used in devising purposeful
marketing strategies.
According to Business Insider, the business ambitions of Amazon are set to plummet
the profitability of various retailers even more. Retail Giants like BestBuy, Macy’s,
Nordstrom and Sears are already making a lot more effort to counter the extensively
growing dominance of Amazon in the industry (Taylor, 2017). The article goes on to
state that internet retailers led by Amazon have increased their revenue in apparel by
$27.8 billion since 2005 as department stores have lost $29.6 billion in the same course
of time (Taylor, 2017).
As mobile devices become the hub of various activities across the digital spectrum, it
is noteworthy to mention that a sound investment in mobile marketing based on
location, can help the businesses reclaim the right to be convenient and time-efficient
while offering incentives that draw attention from the busy minds of the modern
consumer to what they have been missing all around based on the dimensions of time
and space.
To reach a final conclusion and answer the research question based on the discussion
in this work and the case studies it can be argued that by undertaking a solid location-
based advertising strategy to run marketing campaigns, businesses can be thriving by
50
increasing awareness about the value that they can offer for the fair and secure use of
consumers’ location data and reap the benefits of mobile devices acting as the
communication channel to deliver timely adverts containing incentives that meet the
needs of mobile users relative to location with a message of convenience and mutual
prosperity.
6.2. Recommendations
The recommendations that can be made based on this study for future research are
mainly in line with the gap in research done in location-based mobile marketing. To be
more precise, the need for more user-centric studies in the subject as the literature
review suggests. However, if researchers can undertake a more practical approach and
perform experimental studies that evaluate the behavior of the mobile users given the
proper conditions, it can also help fill the gap in research for LBA in mobile marketing
(Bauer & Strauss, 2016). By conducting an experimental research the dependent
variable can be directly set as the revenue and thus provide a clearer image of the end
result which can be measured on numeric basis to provide numbers in terms of income.
A suggestion to be made for researchers who might be interested in doing research in
the same manner as this thesis, assessing the attitude, can be made by including more
predictors other than incentives. This approach allows for a more comprehensive
framework where permission and privacy concerns are taken into account and their
effects are included in the survey alongside the incentives. Even though in this study,
incentives are meant as the main tool for marketers and advertisers to overcome the
obstacle of concerns for violation of privacy, including privacy and other factors such
as informative advertising, personalization, permission and irritability factors, the
research can identify how several factors influence the acceptance of location-based
mobile marketing.
Business psychology, economic sociology and marketing are the most relevant
disciplines to bring the research in location-based advertising on mobile devices
forward (Bauer & Strauss, 2016). More empirical research in LBA, the attribution of
LBA with other aspects such as time namely an event, providing ads that act as
recommender systems and finally in-car LBA as cars can be regarded as highly private
assets such mobile phones can be recommended as guidelines for future studies (Bauer
& Strauss, 2016).
51
As the idea of targeted advertising is directly associated with purchase behavior or
rather identifying the needs of the consumers according to the patterns they leave
behind, mobile ads based on location have more to do with psychological aspects of the
consumers’ mind than any other factor.
Since there are more than enough studies done regarding the technology of location-
based mobile marketing, the same studies can help establish the framework for more
studies in social experiments that cover the issues in location-based mobile marketing
by producing reliable and measurable data that is derived exactly from the behavior of
the mobile users rather than their opinion. If funding is provided such studies can raise
the curiosity of marketers community as a whole and set practical examples of using
location technology where results can be comprehended as a means for reliable
strategies in advertising that are founded on real data.
52
7. List of References
Abowd GD (2012) What next, ubicomp? Celebrating an intellectual
disappearing act. In: 2012 ACM conference on ubiquitous computing
(UbiComp 2012), Pittsburgh, 5–8 September 2012. ACM:31–40 [Online]
Available from: http://dl.acm.org/citation.cfm?doid=2370216.2370222
(Accessed: 18 July 2017)
Ashwini B M, Usha J (2014) Location Based Services: Positioning
Techniques and its Applications. International Journal of Application or
Innovation in Engineering & Management (IJAIEM) Volume 3, Issue 1:176-
183 [Online] Available from: http://www.ijaiem.org/V3I1.html (Accessed: 16
July 2017)
Bauer C, Strauss C (2016) Location-based advertising on mobile devices: A
literature review and analysis. University of Economics, Wien, Austria.
Brimicombe A, LI C, (2009) Location-Based Services and Geo-Information
Engineering, John Wiley & Sons.
Bryman A, Bell E. (2012) Business Research Methods 3rd edition. OUP
Oxford
Chen H, Chiang R, Storey V (2012) Business Intelligence and Analytics:
From Big Data to Big Impact. MIS Quarterly Vol. 36 No. 4:1165-1188
[Online] Available from: https://misq.org/contents-36-4/ (Accessed: 20 July
2017)
Conti N, Jennett C, Maestre J, Sasse MA (2012) When did my mobile turn into
a ’sellphone’? A study of consumer responses to tailored smart phone ads. In:
BCS HCI, People & Computers XXVI (HCI 2012), Birmingham, 12–14
September 2012. British Computer Society Swinton, pp 215–220 [Online]
Available from: http://ewic.bcs.org/content/ConWebDoc/47793 (Accessed: 2
July 2017)
Creswell J. W (2003) Research Design: Qualitative, Quantitative, and Mixed
Methods Approaches. SAGE Publications.
Danielsoper Statistics Calculator (2017) [Online] Available from:
http://www.danielsoper.com/statcalc/default.aspx (Accessed: 24 August 2017)
Dhar S, Varshney U (2011) Challenges and business models for mobile
location-based services and advertising. Communications of the ACM CACM
Homepage archive Volume 54 Issue 5 Pages 121-128. [Online] Available
from: http://dl.acm.org/citation.cfm?doid=1941487.1941515 (Accessed: 10
July 2017)
European Global Navigation Satellite Systems Agency (EGNSSA) (2017)
Location-based Services, Market Report Issue 5. [Online] Available from:
https://www.gsa.europa.eu/gnss-market-report-issue-5 (Accessed: 28 July
2017)
Firth, J. (2015) Smartphones as Locative Media. Polity Press.
Haddadi H, Hui P, Brown I (2010) Mobiad: private and scalable mobile
advertising. In: 5th ACM international workshop on mobility in the evolving
53
internet architecture (MobiArch 2010), Chicago, 24 September 2010. ACM,
pp 33–38. [Online] Available from:
http://dl.acm.org/citation.cfm?doid=1859983.1859993 (Accessed: 16 August
2017)
Hopkins J, Turner J (2012) Go Mobile: Location-Based Marketing, Apps,
Mobile Optimized Ad Campaigns, 2D Codes and Other Mobile Strategies to
Grow Your Business. John Wiley and Sons Inc.
Kelley PG, Benisch M, Cranor LF, Sadeh N (2011) When are users
comfortable sharing locations with advertisers? In: SIGCHI conference on
human factors in computing systems (CHI 2011), Vancouver,7–12 May 2011.
ACM, pp 2449–2451. [Online] Available from:
http://dl.acm.org/citation.cfm?doid=1978942.1979299 (Accessed: 14 July
2017)
Kotler P, Hassell M, Armstrong G (2011) Principles of Marketing. Pearson.
Küpper, A. (2005) Location-based Services, Fundamentals and Operation.
John Wiley & Sons, West Sussex
Laerd Statistics (2013) Binomial Logistic Regression Using SPSS. [Online]
Available from: https://statistics.laerd.com/spss-tutorials/binomial-logistic-
regression-using-spss-statistics.php (Accessed: 4 September 2017)
Leedy P, Ormod J E (2010) Practical Research: Planning and Design Ninth
Edition. Pearson.
Magedanz T, Simoes J (2009) What happens when you mix social networks,
contexts andmobile advertising in the same glass? Int J Mob Mark 4(2):52–60
[Online] Available from: http://publica.fraunhofer.de/documents/N-
147726.html (Accessed: 14 July 2017)
Manyika J, Dobbs R, Chui M, Brown B, Roxburgh C, Bughin J (2011) Big
Data: The Next Frontier for Innovation, Competition, and Productivity.
McKinsey Global Institute.
Merisavo M, Kajalo S, Karjluoto H, Virtanen V, Salmenkivi S, Raulas M,
Leppaeniemi M (2007) An empirical study of the drivers of consumer
acceptance of mobile advertising. J Interact Advert 7(2):41–50. [Online]
Available from:
http://www.tandfonline.com/doi/abs/10.1080/15252019.2007.10722130
(Accessed: 25 July 2017)
Placecast (2012) Pizza Hut’s location-based mobile campaign ‘4.4 times more
effective than TV’ [Online]. Available from:
http://placecast.net/docs/research_casestudy_pizzahut.pdf (Accessed: 7
August 2017)
Placecast (2013) A North American Auto Brand Targets Auto-intenders
Around Car Dealer Locations [Online]. Available from:
http://placecast.net/case-studies/auto-dealer-mobile-ads.html (Accessed: 7
August 2017)
Purcell, B. (2013) The emergence of “big data” technology and analytics.
54
Journal of Technology Research:1-6 [Online] Available from:
https://www.researchgate.net/publication/256888059_Emergence_of_Big_Dat
a_technology_and_analytics (Accessed: 26 August 2017)
Rao B, Minakakis L (2003) Evolution of Mobile Location-based Services.
Communications of the ACM 46 No.12:61-65. [Online] Available from:
http://dl.acm.org/citation.cfm?id=953482&dl=ACM&coll=DL&CFID=98619
1880&CFTOKEN=32184411 (Accessed: 5 August 2017)
Saunders M, Lewis P, Thornhill A (2009) Research Methods for Business
Students Fifth Edition. Pearson Education Limited.
Statler, S. (2016) Beacon Technologies: The Hitchhiker's Guide to the
Beacosystem. Apress Media.
Steiniger S, Neun M, Edwardes A, (2006) Foundations of Location Based
Services. Lesson 1, CartouCHe 1- Lecture Notes on LBS, V. 1.0. [Online]
Available from:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.1844&rep=rep1
&type=pdf (Accessed: 10 August 2017)
Surgue, P. (2015) Consumers Losing Confidence in Retailers’ Ability to
Effectively Leverage Personal Data, According to Boxever Survey [Online]
Available from: http://www.boxever.com/press-release-consumers-losing-
confidence-in-retailers-ability-to-effectively-leverage-personal-data-
according-to-boxever-survey/ (Accessed: 1 July 2017)
Taylor K, (2017) Business Insider: Amazon is killing these 7 companies
[Online] Available from: http://www.businessinsider.de/amazon-is-killing-
these-7-companies-2017-7?op=1 (Accessed: 2 September 2017)
TechChurch (2017) Face ID will work with Apple Pay third party apps
[Online] Available from: https://techcrunch.com/2017/09/12/faceid-will-work-
with-apple-pay-third-party-apps/ (Accessed: 17 September 2017)
Thumbvista (2015) Geofencing & Mobile Display Ads Case Study – Dunkin
Donuts [Online] Available from: https://thumbvista.com/2015/06/geofencing-
mobile-display-ads-case-study-dunkin-donuts/ (Accessed: 15 August 2017)
Turnstyle (2016) Flip Flop Shops Case Study [Online] Available from:
https://getturnstyle.com/case-studies-guides-whitepapers/ (Accessed: 10
August 2017)
Turnstyle (2016) Mondelez International & Couche-Tard Case Study [Online]
Available from: https://getturnstyle.com/case-studies-guides-whitepapers/
(Accessed: 10 August 2017)
Turnstyle (2016) Tokyo Smoke Case Study [Online] Available from:
https://getturnstyle.com/case-studies-guides-whitepapers/ (Accessed: 10
August 2017)
Unni R, Harmon R (2007) Perceived effectiveness of push vs. pull mobile
location-based advertising. J Interact Advert 7(2):28–40. [Online] Available
from: http://www.tandfonline.com/doi/abs/10.1080/15252019.2007.10722129
(Accessed: 2 August 2017)
55
Verrinder J. (2011) Research: Location-based analytics market 'worth $9bn in
five years [Online] Available from: https://www.research-
live.com/article/news/locationbased-analytics-market-worth-9bn-in-five-
years/id/4005699 (Accessed: 15 June 2017)
Ward, C. (2016) Location-based marketing: What are the chief challenges?
[Online] Available from:
https://www.mycustomer.com/marketing/technology/location-based-
marketing-what-are-the-chief-challenges (Accessed: 20 June 2017)
Yuan S-T, Tsao YW (2003) A recommendation mechanism for contextualized
mobile advertising. Expert Syst Appl 24(4):399–414. [Online] Available from:
http://www.sciencedirect.com/science/article/pii/S0957417402001896?via%3
Dihub (Accessed: 19 July 2017)
Zickuhr, K (2013) Location-based Services. Pew Research Center. [Online]
Available from: http://www.pewinternet.org/2013/09/12/location-based-
services/ (Accessed: 15 July 2017)
56
8. Appendices
8.1. Questionnaire
*You are only asked to take part if you own a smartphone*
Thank you for taking the time to participate in this survey. I am a student of Master of
Business Administration at the Anhalt University of Applied Sciences and I am
conducting a questionnaire survey regarding location-based mobile marketing.
Location-based mobile marketing is defined as the ads delivered to you on your phone
based on where you are. For example, a supermarket can send you an ad about the fresh
vegetables that came in today; a clothes store can send an offer on your phone about
the new sales deals when you are near their location.
It is worth mentioning that your participation will be completely anonymous. The
survey will take approximately 10 minutes.
If you have any further questions, please do not hesitate to contact me via the following
email: [email protected].
*Required
1. Do you have location services turned on in the settings of your smartphone? *
1. Yes
2. No
2. Do you use location apps such as Google Maps, Yelp, Runtastic, Foursquare,
Waze and others on your smartphone? *
1. Yes
2, No
3. Could you mark how often you use these connectivity types? *
Most of the times Sometimes Rarely Almost never
Wi-Fi
Bluetooth
Cellular Data
Near-field Communication (NFC)
57
4. Please rank these offers as an exchange for your location data from the most
interesting (first) to the least interesting (last). *
First Second Third Last
Store Credit
Loyalty Points
Cash Rewards
Coupons
5. Would you allow your location data to be shared if you were compensated
with some form of value? *
1. Yes
2. No
What is your stance on the following statements?
1. “I like receiving mobile ads as long as they are relevant to my needs and
preferences.” *
1. Strongly Agree
2. Agree
3. Neutral
4. Disagree
5. Strongly Disagree
2. “I find mobile adverts annoying in general.” *
1. Strongly Agree
2. Agree
3. Neutral
4. Disagree
5. Strongly Disagree
3. “It is easy for me to find deals during non-sale periods.” *
1. Strongly Agree
2. Agree
3. Neutral
4. Disagree
5. Strongly Disagree
58
4. “I like receiving digital coupons that are still valid after a certain amount of
time if I’m not using them right away.” *
1. Strongly Agree
2. Agree
3. Neutral
4. Disagree
5. Strongly Disagree
5. “Being informed of deals and offers on my smartphone is more time-efficient
than going around shopping mall to find them.” *
1. Strongly Agree
2. Agree
3. Neutral
4. Disagree
5. Strongly Disagree
6. “I’d rather receive offers and coupons via one app rather than several apps
from several brands.” *
1. Strongly Agree
2. Agree
3. Neutral
4. Disagree
5. Strongly Disagree
I am…
Gender*
1. Male
2. Female
Age*
*Please choose.
Country of residence*
*Please choose.
59
9. Declaration of Authenticity
I hereby declare that I have prepared the dissertation submitted independently and no
other means and sources apart from ones specified were applied. Furthermore I have
not made payments to third parties for any part of the submitted dissertation. The paper
has not been submitted in a different degree program at the same time nor in a similar
form and has also not been published yet as a whole.
Berlin, 18.09.2017
Saeed Hajihosseinkhabaz
